Dataset Information

Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies.

ABSTRACT:

Background

Idiopathic pulmonary fibrosis (IPF) is a rare interstitial lung disease with a poor prognosis that currently lacks effective treatment methods. Preventing the acute exacerbation of IPF, identifying the molecular subtypes of patients, providing personalized treatment, and developing individualized drugs are guidelines for predictive, preventive, and personalized medicine (PPPM / 3PM) to promote the development of IPF. Oxidative stress (OS) is an important pathological process of IPF. However, the relationship between the expression levels of oxidative stress-related genes (OSRGs) and clinical indices in patients with IPF is unclear; therefore, it is still a challenge to identify potential beneficiaries of antioxidant therapy. Because PPPM aims to recognize and manage diseases by integrating multiple methods, patient stratification and analysis based on OSRGs and identifying biomarkers can help achieve the above goals.

Methods

Transcriptome data from 250 IPF patients were divided into training and validation sets. Core OSRGs were identified in the training set and subsequently clustered to identify oxidative stress-related subtypes. The oxidative stress scores, clinical characteristics, and expression levels of senescence-associated secretory phenotypes (SASPs) of different subtypes were compared to identify patients who were sensitive to antioxidant therapy to conduct differential gene functional enrichment analysis and predict potential therapeutic drugs. Diagnostic markers between subtypes were obtained by integrating multiple machine learning methods, their expression levels were tested in rat models with different degrees of pulmonary fibrosis and validation sets, and nomogram models were constructed. CIBERSORT, single-cell RNA sequencing, and immunofluorescence staining were used to explore the effects of OSRGs on the immune microenvironment.

Results

Core OSRGs classified IPF into two subtypes. Patients classified into subtypes with low oxidative stress levels had better clinical scores, less severe fibrosis, and lower expression of SASP-related molecules. A reliable nomogram model based on five diagnostic markers was constructed, and these markers' expression stability was verified in animal experiments. The number of neutrophils in the immune microenvironment was significantly different between the two subtypes and was closely related to the degree of fibrosis.

Conclusion

Within the framework of PPPM, this work comprehensively explored the role of OSRGs and their mediated cellular senescence and immune processes in the progress of IPF and assessed their capabilities aspredictors of high oxidative stress and disease progression,targets of the vicious loop between regulated pulmonary fibrosis and OS for targeted secondary and tertiary prevention, andreferences for personalized antioxidant and antifibrotic therapies.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-023-00334-4.

SUBMITTER: Yang F

PROVIDER: S-EPMC10439879 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies.

Yang Fan F Wendusubilige Kong Jingwei J Zong Yuhan Y Wang Manting M Jing Chuanqing C Ma Zhaotian Z Li Wanyang W Cao Renshuang R Jing Shuwen S Gao Jie J Li Wenxin W Wang Ji J

The EPMA journal 20230731 3

<h4>Background</h4>Idiopathic pulmonary fibrosis (IPF) is a rare interstitial lung disease with a poor prognosis that currently lacks effective treatment methods. Preventing the acute exacerbation of IPF, identifying the molecular subtypes of patients, providing personalized treatment, and developing individualized drugs are guidelines for predictive, preventive, and personalized medicine (PPPM / 3PM) to promote the development of IPF. Oxidative stress (OS) is an important pathological process o ...[more]

PMID: 37605652

Similar Datasets

Project description:BackgroundAtherosclerosis is a major contributor to morbidity and mortality worldwide. Although several molecular markers associated with atherosclerosis have been developed in recent years, the lack of robust evidence hinders their clinical applications. For these reasons, identification of novel and robust biomarkers will directly contribute to atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). This integrative analysis aimed to identify critical genetic markers of atherosclerosis and further explore the underlying molecular immune mechanism attributing to the altered biomarkers.MethodsGene Expression Omnibus (GEO) series datasets were downloaded from GEO. Firstly, differential expression analysis and functional analysis were conducted. Multiple machine-learning strategies were then employed to screen and determine key genetic markers, and receiver operating characteristic (ROC) analysis was used to assess diagnostic value. Subsequently, cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) and a single-cell RNA sequencing (scRNA-seq) data were performed to explore relationships between signatures and immune cells. Lastly, we validated the biomarkers' expression in human and mice experiments.ResultsA total of 611 overlapping differentially expressed genes (DEGs) included 361 upregulated and 250 downregulated genes. Based on the enrichment analysis, DEGs were mapped in terms related to immune cell involvements, immune activating process, and inflaming signals. After using multiple machine-learning strategies, dehydrogenase/reductase 9 (DHRS9) and protein tyrosine phosphatase receptor type J (PTPRJ) were identified as critical biomarkers and presented their high diagnostic accuracy for atherosclerosis. From CIBERSORT analysis, both DHRS9 and PTPRJ were significantly related to diverse immune cells, such as macrophages and mast cells. Further scRNA-seq analysis indicated DHRS9 was specifically upregulated in macrophages of atherosclerotic lesions, which was confirmed in atherosclerotic patients and mice.ConclusionsOur findings are the first to report the involvement of DHRS9 in the atherogenesis, and the proatherogenic effect of DHRS9 is mediated by immune mechanism. In addition, we confirm that DHRS9 is localized in macrophages within atherosclerotic plaques. Therefore, upregulated DHRS9 could be a novel potential target for the future predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in atherosclerosis.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-022-00289-y.

Project description:ObjectivesSuboptimal health status (SHS), a reversible borderline condition between optimal health status and disease, has been recognized as a main risk factor for non-communicable diseases (NCDs). From the standpoint of predictive, preventive, and personalized medicine (PPPM/3PM), the early detection of SHS provides a window of opportunity for targeted prevention and personalized treatment of NCDs. Considering that immunoglobulin G (IgG) N-glycosylation levels are associated with NCDs, it can be speculated that IgG N-glycomic alteration might occur at the SHS stage.MethodsA case-control study was performed and it consisted of 124 SHS individuals and 124 age-, gender-, and body mass index-matched healthy controls. The IgG N-glycan profiles of 248 plasma samples were analyzed by ultra-performance liquid chromatography instrument.ResultsAfter adjustment for potential confounders (i.e., age, levels of education, physical activity, family income, depression score, fasting plasma glucose, and low-density lipoprotein cholesterol), SHS was significantly associated with 16 IgG N-glycan traits at 5% false discovery rate, reflecting decreased galactosylation and fucosylation with bisecting GlcNAc, as well as increased agalactosylation and fucosylation without bisecting GlcNAc. Canonical correlation analysis showed that glycan peak (GP) 20, GP9, and GP12 tended to be significantly associated with the 5 domains (fatigue, the cardiovascular system, the digestive system, the immune system, and mental status) of SHS. The logistic regression model including IgG N-glycans was of moderate performance in tenfold cross-validation, achieving an average area under the receiver operating characteristic curves of 0.703 (95% confidence interval: 0.637-0.768).ConclusionsThe present findings indicated that SHS-related alteration of IgG N-glycans could be identified at the early onset of SHS, suggesting that IgG N-glycan profiles might be potential biomarker of SHS. The altered SHS-related IgG N-glycans are instrumental for SHS management, which could provide a window opportunity for PPPM in advanced treatment of NCDs and shed light on future studies investigating the pathogenesis of progression from SHS to NCDs.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-022-00278-1.

Project description:BackgroundThe N7-methylguanosine modification (m7G) of the 5' cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM).MethodsThe gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment.ResultsThe m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/.ConclusionThe current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-022-00305-1.

Project description:BackgroundArterial stiffness is a major contributor to morbidity and mortality worldwide. Although several metabolic markers associated with arterial stiffness have been developed, there is limited data regarding whether glycemic control modifies the association between diabetes and arterial stiffness. For these reasons, identification of traits around diabetes will directly contribute to arterial stiffness and atherosclerosis management in the context of predictive, preventive, and personalized medicine (PPPM). Thus, this study aimed to explore the relationship of diabetes and glycemic control status with arterial stiffness in a real-world setting.MethodsData of participants from Beijing Xiaotangshan Examination Center (BXEC) with at least two surveys between 2008 and 2019 were used. Cumulative hazards were presented by inverse probability of treatment weighted (IPTW) Kaplan-Meier curves. Cox models were used to estimate the hazard ratio (HR) and 95% confidence interval (CI). Arterial stiffness was defined as brachial-ankle pulse wave velocity (baPWV) ≥1400 cm/s.ResultsOf 5837 participants, the mean baseline age was 46.5±9.3 years, including 3791 (64.9%) males. During a median follow-up of 4.0 years, 1928 (33.0%) cases of incident arterial stiffness were observed. People with diabetes at baseline had a 48.4% (HR: 1.484, 95% CI: 1.250-1.761) excessive risk of arterial stiffness. Adherence to good glycemic control attenuated the relationship between diabetes and arterial stiffness (HR: 1.264, 95% CI: 0.950-1.681); while uncontrolled diabetes was associated with the highest risk of arterial stiffness (HR: 1.629, 95% CI: 1.323-2.005). Results were consistent using IPTW algorithm and multiple imputed data.ConclusionOur study quantified that diabetes status is closely associated with an increased risk of arterial stiffness and supported that adherence to good glycemic control could attenuate the adverse effect of diabetes on arterial stiffness. Therefore, glucose monitoring and control is a cost-effective strategy for the predictive diagnostics, targeted prevention, patient stratification, and personalization of medical services in early vascular damages and arterial stiffness.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-023-00347-z.

Project description:BackgroundThe human gut microbiota (GM) has been recognized as a significant factor in the development of insomnia, primarily through inflammatory pathways, making it a promising target for therapeutic interventions. Considering the principles of primary prediction, targeted prevention, and personalized treatment medicine (PPPM), identifying specific gut microbiota associated with insomnia and exploring the underlying mechanisms comprehensively are crucial steps towards achieving primary prediction, targeted prevention, and personalized treatment of insomnia.Working hypothesis and methodologyWe hypothesized that alterations in the composition of specific GM could induce insomnia through an inflammatory response, which postulates the existence of a GM-inflammation-insomnia pathway. Mendelian randomization (MR) analyses were employed to examine this pathway and explore the mediative effects of inflammation. We utilized genetic proxies representing GM, insomnia, and inflammatory indicators (including 41 circulating cytokines and C-reactive protein (CRP)), specifically identified from European ancestry. The primary method used to identify insomnia-related GM and examine the medicative effect of inflammation was the inverse variance weighted method, supplemented by the MR-Egger and weighted median methods. Our findings have the potential to identify individuals at risk of insomnia through screening for GM imbalances, leading to the development of targeted prevention and personalized treatment strategies for the condition.ResultsNine genera and three circulating cytokines were identified to be associated with insomnia; only the associations of Clostridium (innocuum group) and β-NGF on insomnia remained significant after the FDR test, OR = 1.08 (95% CI = 1.04-1.12, P = 1.45 × 10-4, q = 0.02) and OR = 1.06 (95% CI = 1.02-1.10, P = 1.06 × 10-3, q = 0.04), respectively. CRP was associated with an increased risk of insomnia, OR = 1.05 (95% CI = 1.01-1.10, P = 6.42 × 10-3). CRP mediated the association of Coprococcus 1, Holdemania, and Rikenellaceae (RC9gut group) with insomnia. No heterogeneity or pleiotropy were detected.ConclusionsOur study highlights the role of specific GM alterations in the development of insomnia and provides insights into the mediating effects of inflammation. Targeting these specific GM alterations presents a promising avenue for advancing the transition from reactive medicine to PPPM in managing insomnia, potentially leading to significant clinical benefits.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-023-00345-1.

Dataset Information

Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies.

Background

Methods

Results

Conclusion

Supplementary information

Publications

Identifying oxidative stress-related biomarkers in idiopathic pulmonary fibrosis in the context of predictive, preventive, and personalized medicine using integrative omics approaches and machine-learning strategies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets