DNA methylation exploration for ARDS: a multi-omics and multi-microarray interrelated analysis

Advancing omics technologies in acute respiratory distress syndrome: paving the way for personalized medicine

Despite advances in critical care, acute respiratory distress syndrome (ARDS) remains a potentially life-threatening condition with high mortality. The heterogeneous nature of ARDS, caused by diverse etiologies, poses considerable challenges to accurate diagnosis, treatment, and prognosis. Conventional methods often fail to elucidate the pathophysiology of ARDS, thus limiting therapeutic efficacy. However, recent advances in omics technologies, including genomics, transcriptomics, proteomics, metabolomics, lipidomics, and epigenomics, have provided deeper insights into ARDS mechanisms. Genomic studies have identified genetic variants associated with ARDS susceptibility, such as polymorphisms in genes encoding angiotensin-converting enzyme, surfactant proteins, toll-like receptor 4, interleukin-6, Fas/FasL, and vascular endothelial growth factor, offering potential therapeutic targets. Transcriptomic and proteomic reveal distinct biomarker profiles associated with ARDS pathogenesis, including dysregulated inflammatory signaling, epithelial and endothelial barrier dysfunction, and compromised immune responses. Metabolomics has highlighted biomarkers, such as phenylalanine and choline, aiding in severity assessment, subphenotype stratification, and treatment response prediction. Lipidomics has uncovered disruptions in lipid metabolism, including altered phospholipids, sphingolipids, and eicosanoids, with key lipid species such as lysophosphatidylcholine and ceramide emerging as biomarkers for severity and outcomes. Epigenomics explores DNA methylation, histone modifications, and non-coding RNAs, revealing their role in regulating inflammation, immune responses, and tissue repair in ARDS. These epigenetic changes hold promise for biomarker discovery and personalized therapy. Integrating these omics technologies advances our understanding of ARDS pathophysiology, enabling precision medicine approaches. This review examines the latest advancements in omics research related to ARDS, emphasizing its role in developing personalized diagnostics and therapeutic strategies to improve disease monitoring, prognosis, and treatment outcomes.

Advancements in omics technologies: Molecular mechanisms of acute lung injury and acute respiratory distress syndrome (Review)

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) is an inflammatory response arising from lung and systemic injury with diverse causes and associated with high rates of morbidity and mortality. To date, no fully effective pharmacological therapies have been established and the relevant underlying mechanisms warrant elucidation, which may be facilitated by multi‑omics technology. The present review summarizes the application of multi‑omics technology in identifying novel diagnostic markers and therapeutic strategies of ALI/ARDS as well as its pathogenesis.

Identifying the signature of NAD+ metabolism-related genes for immunotherapy of gastric cancer

NAD (Nicotinamide Adenine Dinucleotide) -related metabolic reprogramming in tumor cells involves multiple vital cellular processes. However, the role of NAD metabolism in immunity and the prognosis of gastric cancer (GC) remains not elucidated. Here we identified and clustered 33 NAD + metabolism-related genes (NMRGs) based on 808 GC samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Survival analysis between different groups found a poor prognosis in the GC patients with high NMRGs expression. Gene SGCE, APOD, and PPP1R14A were identified and performed high expression in GC samples, while the qRT-PCR results further confirmed that their expression levels in GC cell lines were significantly higher than those from normal human gastric mucosa epithelial cells. Based on the single-cell analysis, Gene SGCE, APOD, and PPP1R14A can potentially be novel biomarkers of tumor-associated fibroblasts (CAFs). In parallel, the proliferation and migration of GC cells were significantly hampered following the knockdown of SGCE, APOD, and PPP1R14A, particularly APOD, we confirmed that APOD knockdown can inhibit β-catenin and N-cadherin expression, while promote E-cadherin expression. This study unveils a novel NMRGs-related gene signature, highlighting APOD as a prognostic biomarker linked to the tumor microenvironment. APOD drives GC cell proliferation and metastasis through the Wnt/β-catenin/EMT signaling pathway, establishing it as a promising therapeutic target for GC patients.

Sociodemographic and Lifestyle Factors and Epigenetic Aging in US Young Adults: NIMHD Social Epigenomics Program

Importance

Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most research is based on samples of older adults who already have measurable chronic disease.

Objective

To investigate whether and how sociodemographic and lifestyle characteristics are associated with biological aging in a younger adult sample across a wide array of epigenetic clock measures.

Design, Setting, and Participants

This cohort study was conducted using data from the National Longitudinal Study of Adolescent to Adult Health, a US representative cohort of adolescents in grades 7 to 12 in 1994 followed up for 25 years to 2018 over 5 interview waves. Participants who provided blood samples at wave V (2016-2018) were analyzed, with samples tested for DNA methylation (DNAm) in 2021 to 2024. Data were analyzed from February 2023 to May 2024.

Exposure

Sociodemographic (sex, race and ethnicity, immigrant status, socioeconomic status, and geographic location) and lifestyle (obesity status by body mass index [BMI] in categories of reference range or underweight [<25], overweight [25 to <30], obesity [30 to <40], and severe obesity [≥40]; exercise level; tobacco use; and alcohol use) characteristics were assessed.

Main Outcome and Measure

Biological aging assessed from banked blood DNAm using 16 epigenetic clocks.

Results

Data were analyzed from 4237 participants (mean [SD] age, 38.4 [2.0] years; percentage [SE], 51.3% [0.01] female and 48.7% [0.01] male; percentage [SE], 2.7% [<0.01] Asian or Pacific Islander, 16.7% [0.02] Black, 8.7% [0.01] Hispanic, and 71.0% [0.03] White). Sociodemographic and lifestyle factors were more often associated with biological aging in clocks trained to estimate morbidity and mortality (eg, PhenoAge, GrimAge, and DunedinPACE) than clocks trained to estimate chronological age (eg, Horvath). For example, the β for an annual income less than $25 000 vs $100 000 or more was 1.99 years (95% CI, 0.45 to 3.52 years) for PhenoAgeAA, 1.70 years (95% CI, 0.68 to 2.72 years) for GrimAgeAA, 0.33 SD (95% CI, 0.17 to 0.48 SD) for DunedinPACE, and -0.17 years (95% CI, -1.08 to 0.74 years) for Horvath1AA. Lower education, lower income, higher obesity levels, no exercise, and tobacco use were associated with faster biological aging across several clocks; associations with GrimAge were particularly robust (no college vs college or higher: β = 2.63 years; 95% CI, 1.67-3.58 years; lower vs higher annual income: <$25 000 vs ≥$100 000: β = 1.70 years; 95% CI, 0.68-2.72 years; severe obesity vs no obesity: β = 1.57 years; 95% CI, 0.51-2.63 years; no weekly exercise vs ≥5 bouts/week: β = 1.33 years; 95% CI, 0.67-1.99 years; current vs no smoking: β = 7.16 years; 95% CI, 6.25-8.07 years).

Conclusions and Relevance

This study found that important social and lifestyle factors were associated with biological aging in a nationally representative cohort of younger adults. These findings suggest that molecular processes underlying disease risk may be identified in adults entering midlife before disease is manifest and inform interventions aimed at reducing social inequalities in heathy aging and longevity.

Comprehensive bioinformatics analysis unveils THEMIS2 as a carcinogenic indicator related to immune infiltration and prognosis of thyroid cancer

The aim of this study was to identify biomarkers associated with the initiation and prognosis of thyroid cancer and elucidate the underlying pathogenic mechanisms. We obtained expression profiles and clinical information from the Cancer Genome Atlas (TCGA)-THCA and three datasets (GSE53157, GSE82208, and GSE76039). The three microarray datasets were combined using Perl and the sva package in R and termed 'merged dataset'. Weighted gene co-expression network analysis (WGCNA) identified 15 gene co-expression modules in the merged dataset and 235 hub genes. Venn diagram analysis revealed 232 overlapping genes between the merged and THCA datasets. Overlapping genes were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The least absolute shrinkage and selection operator (LASSO) regression identified THEMIS2 as a candidate hub gene. Cox, Kaplan-Meier (K-M) survival and gene set enrichment analysis (GSEA) confirmed the correlation of THEMIS2 with overall survival, its enrichment in immunologic processes, and its association with the p53 and JAK-STAT signaling pathways. Its expression was positively correlated with those of immune checkpoints and the infiltration level of immune cells. Receiver operating characteristic curve (ROC) analysis confirmed that THEMIS2, a diagnostic biomarker, could distinguish between tumor and normal specimens. The nomogram (ROC or DCA) model containing THEMIS2, age, and stage predicted favourable prognoses. Thus, THEMIS2 was a biomarker of immune infiltration and prognosis in thyroid cancer.

The Sociodemographic and Lifestyle Correlates of Epigenetic Aging in a Nationally Representative U.S. Study of Younger Adults

Importance

Epigenetic clocks represent molecular evidence of disease risk and aging processes and have been used to identify how social and lifestyle characteristics are associated with accelerated biological aging. However, most of this research is based on older adult samples who already have measurable chronic disease.

Objective

To investigate whether and how sociodemographic and lifestyle characteristics are related to biological aging in a younger adult sample across a wide array of epigenetic clock measures.

Design

Nationally representative prospective cohort study.

Setting

United States (U.S.).

Participants

Data come from the National Longitudinal Study of Adolescent to Adult Health, a national cohort of adolescents in grades 7-12 in U.S. in 1994 followed for 25 years over five interview waves. Our analytic sample includes participants followed-up through Wave V in 2016-18 who provided blood samples for DNA methylation (DNAm) testing (n=4237) at Wave V.

Exposure

Sociodemographic (sex, race/ethnicity, immigrant status, socioeconomic status, geographic location) and lifestyle (obesity status, exercise, tobacco, and alcohol use) characteristics.

Main Outcome

Biological aging assessed from blood DNAm using 16 epigenetic clocks when the cohort was aged 33-44 in Wave V.

Results

While there is considerable variation in the mean and distribution of epigenetic clock estimates and in the correlations among the clocks, we found sociodemographic and lifestyle factors are more often associated with biological aging in clocks trained to predict current or dynamic phenotypes (e.g., PhenoAge, GrimAge and DunedinPACE) as opposed to clocks trained to predict chronological age alone (e.g., Horvath). Consistent and strong associations of faster biological aging were found for those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use.

Conclusions and Relevance

Our study found important social and lifestyle factors associated with biological aging in a nationally representative cohort of younger-aged adults. These findings indicate that molecular processes underlying disease risk can be identified in adults entering midlife before disease is manifest and represent useful targets for interventions to reduce social inequalities in heathy aging and longevity.

Key Points

Question: Are epigenetic clocks, measures of biological aging developed mainly on older-adult samples, meaningful for younger adults and associated with sociodemographic and lifestyle characteristics in expected patterns found in prior aging research?Findings: Sociodemographic and lifestyle factors were associated with biological aging in clocks trained to predict morbidity and mortality showing accelerated aging among those with lower levels of education and income, and those with severe obesity, no weekly exercise, and tobacco use.Meaning: Age-related molecular processes can be identified in younger-aged adults before disease manifests and represent potential interventions to reduce social inequalities in heathy aging and longevity.

The innovative checkpoint inhibitors of lung adenocarcinoma, cg09897064 methylation and ZBP1 expression reduction, have implications for macrophage polarization and tumor growth in lung cancer

Lung cancer, a prevalent and aggressive disease, is characterized by recurrence and drug resistance. It is essential to comprehend the fundamental processes and discover novel therapeutic objectives for augmenting treatment results. Based on our research findings, we have identified a correlation between methylation of cg09897064 and decreased expression of ZBP1, indicating a link to unfavorable prognosis in patients with lung cancer. Furthermore, these factors play a role in macrophage polarization, with ZBP1 upregulated in M1 macrophages compared to both M0 and M2 polarized macrophages. We observed cg09897064 methylation in M2 polarization, but not in M0 and M1 polarized macrophages. ATACseq analysis revealed closed chromatin accessibility of ZBP1 in M0 polarized macrophages, while open accessibility was observed in both M1 and M2 polarized macrophages. Our findings suggest that ZBP1 is downregulated in M0 polarized macrophages due to closed chromatin accessibility and downregulated in M2 polarized macrophages due to cg09897064 methylation. Further investigations manipulating cg09897064 methylation and ZBP1 expression through overexpression plasmids and shRNAs provided evidence for their role in modulating macrophage polarization and tumor growth. ZBP1 inhibits M2 polarization and suppresses tumor growth, while cg09897064 methylation promotes M2 polarization and macrophage-induced tumor growth. In mechanism investigations, we found that cg09897064 methylation impairs CEBPA binding to the ZBP1 promoter, leading to decreased ZBP1 expression. Clinical experiments were conducted to validate the correlation between methylation at cg09897064, ZBP1 expression, and macrophage M2 polarization. Targeting these factors may hold promise as a strategy for developing innovative checkpoint inhibitors in lung cancer treatment.

Identification of the hub genes in polycystic ovary syndrome based on disease-associated molecule network

Revealing the key genes involved in polycystic ovary syndrome (PCOS) and elucidating its pathogenic mechanism is of extreme importance for the development of targeted clinical therapy for PCOS. Investigating disease by integrating several associated and interacting molecules in biological systems will make it possible to discover new pathogenic genes. In this study, an integrative disease-associated molecule network, combining protein-protein interactions and protein-metabolites interactions (PPMI) network was constructed based on the PCOS-associated genes and metabolites systematically collected. This new PPMI strategy identified several potential PCOS-associated genes, which have unreported in previous publications. Moreover, the systematic analysis of five benchmarks data sets indicated the DERL1 was identified as downregulated in PCOS granulosa cell and has good classification performance between PCOS patients and healthy controls. CCR2 and DVL3 were upregulated in PCOS adipose tissues and have good classification performance. The expression of novel gene FXR2 identified in this study is significantly increased in ovarian granulosa cells of PCOS patients compared with controls via quantitative analysis. Our study uncovers substantial differences in the PCOS-specific tissue and provides a plethora of information on dysregulated genes and metabolites that are linked to PCOS. This knowledgebase could have the potential to benefit the scientific and clinical community. In sum, the identification of novel gene associated with PCOS provides valuable insights into the underlying molecular mechanisms of PCOS and could potentially lead to the development of new diagnostic and therapeutic strategies.

Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning

Background

There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using transcriptomic microarrays within twenty-four hours of diagnosis. We used publicly available human whole-blood gene expression arrays of a Berlin-defined pediatric acute respiratory distress syndrome (GSE147902) cohort and a sepsis-triggered AHRF (GSE66099) cohort within twenty-four hours of diagnosis and compared those children with a PaO2/FiO2 < 200 to those with a PaO2/FiO2 ≥ 200.

Results

We used stability selection, a bootstrapping method of 100 simulations using logistic regression as a classifier, to select differentially expressed genes associated with a PaO2/FiO2 < 200 vs. PaO2/FiO2 ≥ 200. The top-ranked genes that contributed to the AHRF signature were selected in each dataset. Genes common to both of the top 1,500 ranked gene lists were selected for pathway analysis. Pathway and network analysis was performed using the Pathway Network Analysis Visualizer (PANEV) and Reactome was used to perform an over-representation gene network analysis of the top-ranked genes common to both cohorts. Changes in metabolic pathways involved in energy balance, fundamental cellular processes such as protein translation, mitochondrial function, oxidative stress, immune signaling, and inflammation are differentially regulated early in pediatric ARDS and sepsis-induced AHRF compared to both healthy controls and to milder acute hypoxemia. Specifically, fundamental pathways related to the severity of hypoxemia emerged and included (1) ribosomal and eukaryotic initiation of factor 2 (eIF2) regulation of protein translation and (2) the nutrient, oxygen, and energy sensing pathway, mTOR, activated via PI3K/AKT signaling.

Conclusions

Cellular energetics and metabolic pathways are important mechanisms to consider to further our understanding of the heterogeneity and underlying pathobiology of moderate and severe pediatric acute respiratory distress syndrome. Our findings are hypothesis generating and support the study of metabolic pathways and cellular energetics to understand heterogeneity and underlying pathobiology of moderate and severe acute hypoxemic respiratory failure in children.

Identification of candidate genes associated with clinical onset of Alzheimer's disease

Background and objective

Alzheimer's disease (AD) is the most common type of dementia, with its pathology like beta-amyloid and phosphorylated tau beginning several years before the clinical onset. The aim is to identify genetic risk factors associated with the onset of AD.

Methods

We collected three microarray data of post-mortem brains of AD patients and the healthy from the GEO database and screened differentially expressed genes between AD and healthy control. GO/KEGG analysis was applied to identify AD-related pathways. Then we distinguished differential expressed genes between symptomatic and asymptomatic AD. Feature importance with logistic regression analysis is adopted to identify the most critical genes with symptomatic AD.

Results

Data was collected from three datasets, including 184 AD patients and 132 healthy controls. We found 66 genes to be differently expressed between AD and the control. The pathway enriched in the process of exocytosis, synapse, and metabolism and identified 19 candidate genes, four of which (VSNL1, RTN1, FGF12, and ENC1) are vital.

Conclusion

VSNL1, RTN1, FGF12, and ENC1 may be the essential genes that progress asymptomatic AD to symptomatic AD. Moreover, they may serve as genetic risk factors to identify high-risk individuals showing an earlier onset of AD.

Bariatric surgery-induced weight loss and associated genome-wide DNA-methylation alterations in obese individuals

BACKGROUND

Obesity is a multifactorial and chronic condition of growing universal concern. It has recently been reported that bariatric surgery is a more successful treatment for severe obesity than other noninvasive interventions, resulting in rapid significant weight loss and associated chronic disease remission. The identification of distinct epigenetic patterns in patients who are obese or have metabolic imbalances has suggested a potential role for epigenetic alterations in causal or mediating pathways in the development of obesity-related pathologies. Specific changes in the epigenome (DNA methylome), associated with metabolic disorders, can be detected in the blood. We investigated whether such epigenetic changes are reversible after weight loss using genome-wide DNA methylome analysis of blood samples from individuals with severe obesity (mean BMI ~ 45) undergoing bariatric surgery.

RESULTS

Our analysis revealed 41 significant (Bonferroni p < 0.05) and 1169 (false discovery rate p < 0.05) suggestive differentially methylated positions (DMPs) associated with weight loss due to bariatric surgery. Among the 41 significant DMPs, 5 CpGs were replicated in an independent cohort of BMI-discordant monozygotic twins (the heavier twin underwent diet-induced weight loss). The effect sizes of these 5 CpGs were consistent across discovery and replication sets (p < 0.05). We also identified 192 differentially methylated regions (DMRs) among which SMAD6 and PFKFB3 genes were the top hypermethylated and hypomethylated regions, respectively. Pathway enrichment analysis of the DMR-associated genes showed that functional pathways related to immune function and type 1 diabetes were significant. Weight loss due to bariatric surgery also significantly decelerated epigenetic age 12 months after the intervention (mean =  - 4.29; p = 0.02).

CONCLUSIONS

We identified weight loss-associated DNA-methylation alterations targeting immune and inflammatory gene pathways in blood samples from bariatric-surgery patients. The top hits were replicated in samples from an independent cohort of BMI-discordant monozygotic twins following a hypocaloric diet. Energy restriction and bariatric surgery thus share CpGs that may represent early indicators of response to the metabolic effects of weight loss. The analysis of bariatric surgery-associated DMRs suggests that epigenetic regulation of genes involved in endothelial and adipose tissue function is key in the pathophysiology of obesity.

Epigenetic underpinnings of inflammation: Connecting the dots between pulmonary diseases, lung cancer and COVID-19

Inflammation is an essential component of several respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma and acute respiratory distress syndrome (ARDS). It is central to lung cancer, the leading cancer in terms of associated mortality that has affected millions of individuals worldwide. Inflammation and pulmonary manifestations are also the major causes of COVID-19 related deaths. Acute hyperinflammation plays an important role in the COVID-19 disease progression and severity, and development of protective immunity against the virus is greatly sought. Further, the severity of COVID-19 is greatly enhanced in lung cancer patients, probably due to the genes such as ACE2, TMPRSS2, PAI-1 and furin that are commonly involved in cancer progression as well as SAR-CoV-2 infection. The importance of inflammation in pulmonary manifestations, cancer and COVID-19 calls for a closer look at the underlying processes, particularly the associated increase in IL-6 and other cytokines, the dysregulation of immune cells and the coagulation pathway. Towards this end, several reports have identified epigenetic regulation of inflammation at different levels. Expression of several key inflammation-related cytokines, chemokines and other genes is affected by methylation and acetylation while non-coding RNAs, including microRNAs as well as long non-coding RNAs, also affect the overall inflammatory responses. Select miRNAs can regulate inflammation in COVID-19 infection, lung cancer as well as other inflammatory lung diseases, and can serve as epigenetic links that can be therapeutically targeted. Furthermore, epigenetic changes also mediate the environmental factors-induced inflammation. Therefore, a better understanding of epigenetic regulation of inflammation can potentially help develop novel strategies to prevent, diagnose and treat chronic pulmonary diseases, lung cancer and COVID-19.

Mechanistic Understanding of Lung Inflammation: Recent Advances and Emerging Techniques

Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury characterized by an acute inflammatory response in the lung parenchyma. Hence, it is considered as the most appropriate clinical syndrome to study pathogenic mechanisms of lung inflammation. ARDS is associated with increased morbidity and mortality in the intensive care unit (ICU), while no effective pharmacological treatment exists. It is very important therefore to fully characterize the underlying pathobiology and the related mechanisms, in order to develop novel therapeutic approaches. In vivo and in vitro models are important pre-clinical tools in biological and medical research in the mechanistic and pathological understanding of the majority of diseases. In this review, we will present data from selected experimental models of lung injury/acute lung inflammation, which have been based on clinical disorders that can lead to the development of ARDS and related inflammatory lung processes in humans, including ventilation-induced lung injury (VILI), sepsis, ischemia/reperfusion, smoke, acid aspiration, radiation, transfusion-related acute lung injury (TRALI), influenza, Streptococcus (S.) pneumoniae and coronaviruses infection. Data from the corresponding clinical conditions will also be presented. The mechanisms related to lung inflammation that will be covered are oxidative stress, neutrophil extracellular traps, mitogen-activated protein kinase (MAPK) pathways, surfactant, and water and ion channels. Finally, we will present a brief overview of emerging techniques in the field of omics research that have been applied to ARDS research, encompassing genomics, transcriptomics, proteomics, and metabolomics, which may recognize factors to help stratify ICU patients at risk, predict their prognosis, and possibly, serve as more specific therapeutic targets.

Construction and validation of m6 A RNA methylation regulators associated prognostic model for gastrointestinal cancer

N6-methyladenosine (m6 A) RNA methylation is correlated with carcinogenesis and dynamically possessed through the m6 A RNA methylation regulators. This paper aimed to explore 13 m6 A RNA methylation regulators' role in gastrointestinal cancer (GIC) and determine the risk model and prognosis value of m6 A RNA methylation regulators in GIC. We used several bioinformatics methods to identify the differential expression of m6 A RNA methylation regulators in GIC, constructed a prognostic model, and carried out functional enrichment analysis. Eleven of 13 m6 A RNA methylation regulators were differentially expressed in different clinicopathological characteristics of GIC, and m6 A RNA methylation regulators were nearly associated with GIC. We constructed a risk model based on five m6 A RNA methylation regulators (METTL3, FTO, YTHDF1, ZC3H13, and WTAP); the risk score is an independent prognosis biomarker. Moreover, the five m6 A RNA methylation regulators can also forecast the 1-, 3- and 5-year overall survival through a nomogram. Furthermore, four hallmarks of oxidative phosphorylation, glycolysis, fatty acid metabolism, and cholesterol homoeostasis gene sets were significantly enriched in GIC. m6 A RNA methylation regulators were related to the malignant clinicopathological characteristics of GIC and may be used for prognostic stratification and development of therapeutic strategies.

Novel biomarkers for acute respiratory distress syndrome: genetics, epigenetics and transcriptomics

Acute respiratory distress syndrome (ARDS) can be induced by multiple clinical factors, including sepsis, acute pancreatitis, trauma, intestinal ischemia/reperfusion and burns. However, these factors alone may poorly explain the risk and outcomes of ARDS. Emerging evidence suggests that genomic-based or transcriptomic-based biomarkers may hold the promise to establish predictive or prognostic stratification methods for ARDS, and also to help in developing novel therapeutic targets for ARDS. Notably, genetic/epigenetic variations correlated with susceptibility and prognosis of ARDS and circulating microRNAs have emerged as potential biomarkers for diagnosis or prognosis of ARDS. Although limited by sample size, ethnicity and phenotypic heterogeneity, ongoing genetic/transcriptomic research contributes to the characterization of novel biomarkers and ultimately helps to develop innovative therapeutics for ARDS patients.

A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients

Background

Sepsis is a highly heterogeneous syndrome with stratified severity levels and immune states. Even in patients with similar clinical appearances, the underlying signal transduction pathways are significantly different. To identify the heterogeneities of sepsis from multiple angles, we aimed to establish a combined risk model including the molecular risk score for rapid mortality prediction, pathway risk score for the identification of biological pathway variations, and immunity risk score for guidance with immune-modulation therapy.

Methods

We systematically searched and screened the mRNA expression profiles of patients with sepsis in the Gene Expression Omnibus public database. The screened datasets were divided into a training cohort and a validation cohort. In the training cohort, authentic prognostic predictor characteristics (differentially expressed mRNAs, pathway activity variations and immune cells) were screened for model construction through bioinformatics analysis and univariate Cox regression, and a P value less than 0.05 of univariate Cox regression on 28-day mortality was set as the cut-off value. The combined risk model was finally established by the decision tree algorithm. In the validation cohort, the model performance was assessed and validated by C statistics and the area under the receiver operating characteristic curve (AUC). Additionally, the current models were further compared in clinical value with traditional indicators, including procalcitonin (PCT) and interleukin-8 (IL-8).

Results

Datasets from two sepsis cohort studies with a total of 585 consecutive sepsis patients admitted to two intensive care units were downloaded as the training cohort (n = 479) and external validation cohort (n = 106). In the training cohort, 15 molecules, 20 pathways and 4 immune cells were eventually enrolled in model construction. These prognostic factors mainly reflected hypoxia, cellular injury, metabolic disorders and immune dysregulation in sepsis patients. In the validation cohort, the AUCs of the molecular model, pathway model, immune model, and combined model were 0.81, 0.82, 0.62 and 0.873, respectively. The AUCs of the traditional biomarkers (PCT and IL-8) were 0.565 and 0.585, respectively. The survival analysis indicated that patients in the high-risk group identified by models in the current study had a poor prognosis (P < 0.05). The above results indicated that the models in this study are all superior to the traditional biomarkers for the predicting the prognosis of sepsis patients. Furthermore, the current study provides some therapeutic recommendations for patients with high risk scores identified by the three submodels.

Conclusions

In summary, the present study provides opportunities for bedside tests that could quantitatively and rapidly measure heterogeneous prognosis, underlying biological pathway variations and immune dysfunction in sepsis patients. Further therapeutic recommendations for patients with high risk scores could improve the therapeutic system for sepsis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01552-x.

Identifying the mechanism underlying antidepressant-like effects of loganin by network pharmacology in combination with experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Loganin, an iridoid glycoside, is one of the quality control indexes of Cornus officinalis Sieb. et Zucc. Increasing evidence emphasize the important role of inflammation in the pathology of depression, which links depression with other chronic diseases. Loganin prevents inflammatory response in multiple diseases and reverses depressive-like behaviors. However, the mechanisms underlying antidepressant-like effects of loganin for the treatment of inflammation-associated depression are not utterly understood.

AIM OF THE STUDY

The present study was designed to predict the potential targets of loganin against inflammation-associated depression using a network pharmacology approach.

MATERIALS AND METHODS

Pharmmapper and Uniport were used to predict loganin-related targets. Targets of inflammation were identified through GeneCards databases and Online Mendelian Inheritance in Man (OMIM). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to identify the potential mechanism. Finally, qRT-PCR and ELISA were used to confirm the role of loganin on these targets.

RESULTS

There were 15 nodes in the loganin-inflammation-depression intersection targets network. In the network, the degree value of CTNNB1 was above 3. Among top ten pathways identified by KEGG analysis, Th1/Th2 cell differentiation and IL-17 signaling pathways were related with both inflammation and depression. As indicated by qRT-PCR results, loganin increased CTNNB1 mRNA level. Moreover, loganin elevated M2 markers of microglia but decreased M1 markers of microglia against lipopolysaccharide (LPS), indicated by qRT-PCR results and ELISA results.

CONCLUSION

CTNNB1 was the main target of loganin. Loganin alleviated LPS-induced inflammation through inhibiting M1 polarization of microglia. Our results provide a better understanding of loganin-induced antidepressant-like effects for the treatment of inflammation-associated depression.

ARDS Patients Exhibiting a "Hyperinflammatory Anasarca" Phenotype Could Benefit From a Conservative Fluid Management Strategy

Object: The fluid management strategy in ARDS is not very clear. A secondary analysis of RCT data was conducted to identify patients with ARDS benefitting from a conservative strategy of fluid management. Methods: The data of this study were downloaded from the ARDS network series of randomized controlled trials (Conservative Strategy vs. Liberal Strategy in 2006). Based on the clinical feature of patients, within the first 24 h after admission, clustering was performed using the k-means clustering algorithm to identify the phenotypes of ARDS. Survival was analyzed using the Kaplan-Meier survival analysis to assess the effect of the two fluid management strategies on the 90-day cumulative mortality. Categorical/dichotomic variables were analyzed by the chi-square test. Continuous variables were expressed as the mean and standard deviation and evaluated through a one-way ANOVA. A P-value < 0.05 was defined as the statistically significant cut-off value. Results: A total of 1,000 ARDS patients were enrolled in this unsupervised clustering research study, of which 503 patients were treated with a conservative fluid-management strategy, and 497 patients were treated with a liberal fluid-management strategy. The first 7-day cumulative fluid balance in patients with the conservative strategy and liberal strategy were -136 ± 491 ml and 6,992 ± 502 ml, respectively (P < 0.001). Four phenotypes were found, and the conservative fluid-management strategy significantly improved the 90-day cumulative mortality compared with the liberal fluid-management strategy (HR = 0.532, P = 0.024) in patients classified as "hyperinflammatory anasarca" phenotype (phenotype II). The characteristics of this phenotype exhibited a higher WBC count (20487.51 ± 7223.86/mm3) with a higher incidence of anasarca (8.3%) and incidence of shock (26.6%) at baseline. The furthermore analysis found that the conservative fluid management strategy was superior to the liberal fluid management strategy in avoiding superinfection (10.10 vs. 14.40%, P = 0.037) and returned to assisted breathing (4.60 vs. 16.20%, P = 0.030) in patients classified as "hyperinflammatory anasarca" phenotype. In addition, patients with other phenotypes given the different fluid management strategies did not show significant differences in clinical outcomes. Conclusion: Patients exhibiting a "hyperinflammatory anasarca" phenotype could benefit from a conservative fluid management strategy.

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Background

Aberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.

Methods

Methylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).

Results

We identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HR_indirect = 1.473, P = 0.001, Proportion mediated, 69.10%).

Conclusions

We identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

Genome-Wide DNA Methylation Profiling of Esophageal Squamous Cell Carcinoma from Global High-Incidence Regions Identifies Crucial Genes and Potential Cancer Markers

Epigenetic mechanisms such as aberrant DNA methylation (DNAme) are known to drive esophageal squamous cell carcinoma (ESCC), yet they remain poorly understood. Here, we studied tumor-specific DNAme in ESCC cases from nine high-incidence countries of Africa, Asia, and South America. Infinium MethylationEPIC array was performed on 108 tumors and 51 normal tissues adjacent to the tumors (NAT) in the discovery phase, and targeted pyrosequencing was performed on 132 tumors and 36 NAT in the replication phase. Top genes for replication were prioritized by weighting methylation results using RNA-sequencing data from The Cancer Genome Atlas and GTEx and validated by qPCR. Methylome analysis comparing tumor and NAT identified 6,796 differentially methylated positions (DMP) and 866 differential methylated regions (DMR), with a 30% methylation (Δβ) difference. The majority of identified DMPs and DMRs were hypermethylated in tumors, particularly in promoters and gene-body regions of genes involved in transcription activation. The top three prioritized genes for replication, PAX9, SIM2, and THSD4, had similar methylation differences in the discovery and replication sets. These genes were exclusively expressed in normal esophageal tissues in GTEx and downregulated in tumors. The specificity and sensitivity of these DNAme events in discriminating tumors from NAT were assessed. Our study identified novel, robust, and crucial tumor-specific DNAme events in ESCC tumors across several high-incidence populations of the world. Methylome changes identified in this study may serve as potential targets for biomarker discovery and warrant further functional characterization. SIGNIFICANCE: This largest genome-wide DNA methylation study on ESCC from high-incidence populations of the world identifies functionally relevant and robust DNAme events that could serve as potential tumor-specific markers. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/81/10/2612/F1.large.jpg.

Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

Molecular Mechanisms of Vascular Damage During Lung Injury

A variety of pulmonary and systemic insults promote an inflammatory response causing increased vascular permeability, leading to the development of acute lung injury (ALI), a condition necessitating hospitalization and intensive care, or the more severe acute respiratory distress syndrome (ARDS), a disease with a high mortality rate. Further, COVID-19 pandemic-associated ARDS is now a major cause of mortality worldwide. The pathogenesis of ALI is explained by injury to both the vascular endothelium and the alveolar epithelium. The disruption of the lung endothelial and epithelial barriers occurs in response to both systemic and local production of pro-inflammatory cytokines. Studies that evaluate the association of genetic polymorphisms with disease risk did not yield many potential therapeutic targets to treat and revert lung injury. This failure is probably due in part to the phenotypic complexity of ALI/ARDS, and genetic predisposition may be obscured by the multiple environmental and behavioral risk factors. In the last decade, new research has uncovered novel epigenetic mechanisms that control ALI/ARDS pathogenesis, including histone modifications and DNA methylation. Enzyme inhibitors such as DNMTi and HDACi may offer new alternative strategies to prevent or reverse the vascular damage that occurs during lung injury. This review will focus on the latest findings on the molecular mechanisms of vascular damage in ALI/ARDS, the genetic factors that might contribute to the susceptibility for developing this disease, and the epigenetic changes observed in humans, as well as in experimental models of ALI/ADRS.

Identification prognosis-associated immune genes in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most prevalent malignant tumors worldwide. Immune genes (IGs) have a considerable correlation with tumor initiation and prognosis. The present paper aims to identify the prognosis value of IGs in COAD and conduct a prognosis model for clinical utility. Gene expression data of COAD were downloaded from The Cancer Genome Atlas (TCGA), screening and analyzing differentially expressed IGs by bioinformatics. Core genes were screened by univariate and multivariate Cox regression analyses. Survival analysis was appraised by the Kaplan-Meier method and the log-rank test. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis (GSEA) were used to identify IGs' relevant signal pathways. We predicted the overall survival (OS) by nomogram. Finally, a prognosis model was conducted based on 12 IGs (SLC10A2, CXCL3, NOX4, FABP4, ADIPOQ, IGKV1-33, IGLV6-57, INHBA, UCN, VIP, NGFR, and TRDC). The risk score was an independent prognostic factor, and a nomogram could accurately predict the OS of individual COAD patients. These results were validated in GSE39582, GSE12945, and GSE103479 cohorts. Functional enrichment analysis demonstrated that these IGs are mainly enriched in hormone secretion, hormone transport, lipid transport, cytokine-cytokine receptor interaction, and peroxisome proliferators-activated receptor signaling pathway. In summary, the risk score is an independent prognostic biomarker. We also excavated several IGs related to COAD's survival and maybe potential biomarkers for COAD diagnosis and treatment.

Determination of a "Specific Population Who Could Benefit From Rosuvastatin": A Secondary Analysis of a Randomized Controlled Trial to Uncover the Novel Value of Rosuvastatin for the Precise Treatment of ARDS

Background: The high heterogeneity of acute respiratory distress syndrome (ARDS) contributes to paradoxical conclusions from previous investigations of rosuvastatin for ARDS. Identification of the population (phenotype) that could benefit from rosuvastatin is a novel exploration for the precise treatment. Methods: The patient population for this analysis consisted of unique patients with ARDS enrolled in the SAILS trial (rosuvastatin vs. placebo). Phenotypes were derived using consensus k-means clustering applied to routinely available clinical variables within 6 h of hospital presentation before the patients received placebo or rosuvastatin. The Kaplan-Meier statistic was used to estimate the 90-day cumulative mortality to screen for a specific population that could benefit from rosuvastatin, with a cutoff P < 0.05. Results: The derivation cohort included 585 patients with ARDS. Of the patients with the four derived phenotypes, those with phenotype 3 were classified as the "specific population who could benefit from rosuvastatin" as rosuvastatin resulted in a significant reduction in 90-day cumulative mortality from ARDS [hazard ratio (HR), 0.29; 95% confidence interval (CI), 0.09-0.93; P = 0.027]. Additionally, rosuvastatin markedly improved the days free of cardiovascular failure (10.08 ± 3.79 in the rosuvastatin group vs. 7.31 ± 4.94 in the placebo group, P = 0.01) and coagulation abnormalities (13.65 ± 1.33 vs. 12.15 ± 3.77, P = 0.02) up to day 14 in the phenotype 3 cohort. Phenotype 3 was summarized as Platelethigh & Creatlow phenotype because these patients have a relatively higher platelet count (390.05 ± 79.43 × 109/L) and lower creatinine (1.42 ± 1.08 mg/dL) than do patients classified as other phenotypes. In addition, rosuvastatin seemed to increase 90-day mortality for patients classified as phenotype 4 (HR, 2.76; 95% CI, 0.09-9.93; P = 0.076), with an adverse effect on reducing the days free of renal failure up to day 14 (4.70 ± 4.99 vs. 10.17 ± 4.69, P = 0.01). Patients in phenotype 4 showed relatively severe illness in terms of baseline features, particularly renal failure, with high serum glucose. Therefore, phenotype 4 was defined as APACHEhigh & Serum glucosehigh phenotype. Conclusions: This secondary analysis of the SAILS trial identified that rosuvastatin seems to be harmful for patients classified as APACHEhigh & Serum glucosehigh phenotype, but benefit patients in Platelethigh & Creatlow phenotype, thus uncovering the novel value of rosuvastatin for the precise treatment of ARDS.

Contribution of m6A subtype classification on heterogeneity of sepsis

Background

Sepsis is a highly heterogeneous syndrome with diverse immune status and varied bioprocesses among individuals. The heterogeneity of sepsis could be associated with N6-methyladenosine (m6A) RNA methylation, due to m6A as a common and reversible posttranscriptional RNA modification involved in the regulation of whole bioprocesses. Therefore, we aim to identify m6A induced molecular subtypes of sepsis and furthermore explore the probable mechanism.

Methods

Gene expression datasets with 479 consecutive patients admitted for sepsis to the intensive care unit (ICU) in the Amsterdam Academic Medical Center were included in present study at first. Secondly, twelve m6A methylation regulatory genes were determined via systematic review in published researches. Furthermore, we utilized unsupervised clustering (consensus k means clustering) to identify m6A induced molecular subtypes in sepsis based on m6A prognostic molecular, and assess the association of these subtypes with clinical traits and survival outcomes. Moreover, the probable mechanism and regulatory relationship of m6A in sepsis was also explored through Gene Set Enrichment Analysis (GSEA), Weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) analysis and Co-expression analysis.

Results

Three m6A subtypes with different outcome were identified in sepsis cohort through unsupervised clustering on m6A prognostic molecular, designated Cluster 1/2/3 (log-rank P=0.004). The best outcome was found for patients classified as having cluster 3, and at 28 days, 21 of 144 people with cluster 3 had died [hazard ratio (HR) vs. all other clusters 5.42 (95% CI: 0.359–0.819); P=0.011], compared with 57 of 224 people with cluster 1 (HR 0.579, 95% CI: 0.364–0.920; P=0.037), and 36 of 112 people with cluster 2 (HR 0.477, 95% CI: 0.272–0.833; P=0.003). For exploration of the relationship between m6A subtypes and immunity, the GSEA found that patients in cluster 1 suffered from hyper-activated immunocompetent status; patients in cluster 2 indicated immunosuppressive status; and patients in cluster 3 showed the moderate immune activity (P<0.05). Co-expression analysis furthermore identified 82 immune molecules and 40 autophagy-related molecules could be regulated by prognostic m6A RNA methylation regulators (P<0.05) and correlation coefficient >0.6. In addition, WGCNA and GO analysis indicated that autophagy was significantly and widely activated in patients with cluster 3 (P<0.05).

Conclusions

According to the heterogeneity in m6A methylation regulatory genes, three distinct subtypes in sepsis were identified with different RNA epigenetics, immune status, biological processes and outcomes, which initially uncovered that heterogeneity of sepsis may be largely caused by m6A RNA methylation.