Epma Journal最新文献

Background: Atherosclerosis 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.

Methods: Gene 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.

Results: A 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.

Conclusions: Our 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 information: The online version contains supplementary material available at 10.1007/s13167-022-00289-y.

Background: Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing.

Methods: This investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques-permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)-were applied for explaining the black-box ML models.

Results: Fifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90-0.92) and 0.92 (0.91-0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model's prediction. LIME and SHAP showed similar local feature attribution explanations.

Conclusion: In the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00283-4.

Objectives: Suboptimal 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.

Methods: A 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.

Results: After 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).

Conclusions: The 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 information: The online version contains supplementary material available at 10.1007/s13167-022-00278-1.

Background: Bone metastasis (BM) and skeletal-related events (SREs) happen to advanced lung cancer (LC) patients without warning. LC-BM patients are often passive to BM diagnosis and surgical treatment. It is necessary to guide the diagnosis and treatment paradigm for LC-BM patients from reactive medicine toward predictive, preventive, and personalized medicine (PPPM) step by step.

Methods: Two independent study cohorts including LC-BM patients were analyzed, including the Surveillance, Epidemiology, and End Results (SEER) cohort (n = 203942) and the prospective Fudan University Shanghai Cancer Center (FUSCC) cohort (n = 59). The epidemiological trends of BM in LC patients were depicted. Risk factors for BM were identified using a multivariable logistic regression model. An individualized nomogram was developed for BM risk stratification. Personalized surgical strategies and perioperative care were described for FUSCC cohort.

Results: The BM incidence rate in LC patients grew (from 17.53% in 2010 to 19.05% in 2016). Liver metastasis was a significant risk factor for BM (OR = 4.53, 95% CI = 4.38-4.69) and poor prognosis (HR = 1.29, 95% CI = 1.25-1.32). The individualized nomogram exhibited good predictive performance for BM risk stratification (AUC = 0.784, 95%CI = 0.781-0.786). Younger patients, males, patients with high invasive LC, and patients with other distant site metastases should be prioritized for BM prevention. Spine is the most common site of BM, causing back pain (91.5%), pathological vertebral fracture (27.1%), and difficult walking (25.4%). Spinal surgery with personalized spinal reconstruction significantly relieved pain and improved daily activities. Perioperative inflammation, immune, and nutrition abnormities warrant personalized managements. Radiotherapy needs to be recommended for specific postoperative individuals.

Conclusions: The presence of liver metastasis is a strong predictor of LC-BM. It is recommended to take proactive measures to prevent BM and its SREs, particularly in young patients, males, high invasive LC, and LC with liver metastasis. BM surgery and perioperative management are personalized and required. In addition, adjuvant radiation following separation surgery must also be included in PPPM-guided management.

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Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00270-9.

Preterm birth (PTB) is the leading cause of neonatal death. The essential strategy to prevent PTB is the accurate identification of threatened preterm labor (TPTL) women who will have PTB in a short time (< 7 days). Here, we aim to propose a clinical model to contribute to the effective prediction, precise prevention, and personalized medical treatment for PTB < 7 days in TPTL women through bioinformatics analysis and prospective cohort studies. In this study, the 1090 key genes involved in PTB < 7 days in the peripheral blood of TPTL women were ascertained using WGCNA. Based on this, the biological basis of immune-inflammatory activation (e.g., IFNγ and TNFα signaling) as well as immune cell disorders (e.g., monocytes and Th17 cells) in PTB < 7 days were revealed. Then, four core genes (JOSD1, IDNK, ZMYM3, and IL1B) that best represent their transcriptomic characteristics were screened by SVM and LASSO algorithm. Therefore, a prediction model with an AUC of 0.907 was constructed, which was validated in a larger population (AUC = 0.783). Moreover, the predictive value (AUC = 0.957) and clinical feasibility of this model were verified through the clinical prospective cohort we established. In conclusion, in the context of Predictive, Preventive, and Personalized Medicine (3PM), we have developed and validated a model to predict PTB < 7 days in TPTL women. This is promising to greatly improve the accuracy of clinical prediction, which would facilitate the personalized management of TPTL women to precisely prevent PTB < 7 days and improve maternal-fetal outcomes.

Objectives: Colorectal cancer (CRC) is one of the most common solid tumors worldwide, but its diagnosis and treatment are limited. The objectives of our study were to compare the metabolic differences between CRC patients and healthy controls (HC), and to identify potential biomarkers in the serum that can be used for early diagnosis and as effective therapeutic targets. The aim was to provide a new direction for CRC predictive, preventive, and personalized medicine (PPPM).

Methods: In this study, CRC patients (n = 30) and HC (n = 30) were recruited. Serum metabolites were assayed using an ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) technology. Subsequently, CRC cell lines (HCT116 and HCT8) were treated with metabolites to verify their function. Key targets were identified by molecular docking, thermal shift assay, and protein overexpression/inhibition experiments. The inhibitory effect of celastrol on tumor growth was also assessed, which included IC50 analysis, nude mice xenografting, molecular docking, protein overexpression/inhibition experiments, and network pharmacology technology.

Results: In the CRC group, 15 serum metabolites were significantly different in comparison with the HC group. The level of glycodeoxycholic acid (GDCA) was positively correlated with CRC and showed high sensitivity and specificity for the clinical diagnostic reference (AUC = 0.825). In vitro findings showed that GDCA promoted the proliferation and migration of CRC cell lines (HCT116 and HCT8), and Poly(ADP-ribose) polymerase-1 (PARP-1) was identified as one of the key targets of GDCA. The IC50 of celastrol in HCT116 cells was 121.1 nM, and the anticancer effect of celastrol was supported by in vivo experiments. Based on the potential of GDCA in PPPM, PARP-1 was found to be significantly correlated with the anticancer functions of celastrol.

Conclusion: These findings suggest that GDCA is an abnormally produced metabolite of CRC, which may provide an innovative molecular biomarker for the predictive identification and targeted prevention of CRC. In addition, PARP-1 was found to be an important target of GDCA that promotes CRC; therefore, celastrol may be a potential targeted therapy for CRC via its effects on PARP-1. Taken together, the pathophysiology and progress of tumor molecules mediated by changes in metabolite content provide a new perspective for predictive, preventive, and personalized medical of clinical cancer patients based on the target of metabolites in vivo.Clinical trials registration number: ChiCTR2000039410.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-021-00269-8.

Background: Acupuncture is safe and effective for functional dyspepsia (FD), while its efficacy varies among individuals. Predicting the response of different FD patients to acupuncture treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). In the current study, the individual efficacy prediction models were developed based on the support vector machine (SVM) algorithm and routine clinical features, aiming to predict the efficacy of acupuncture in treating FD and identify the FD patients who were appropriate to acupuncture treatment.

Methods: A total of 745 FD patients were collected from two clinical trials. All the patients received a 4-week acupuncture treatment. Based on the demographic and baseline clinical features of 80% of patients in trial 1, the SVM models were established to predict the acupuncture response and improvements of symptoms and quality of life (QoL) at the end of treatment. Then, the left 20% of patients in trial 1 and 193 patients in trial 2 were respectively applied to evaluate the internal and external generalizations of these models.

Results: These models could predict the efficacy of acupuncture successfully. In the internal test set, models achieved an accuracy of 0.773 in predicting acupuncture response and an R ² of 0.446 and 0.413 in the prediction of QoL and symptoms improvements, respectively. Additionally, these models had well generalization in the independent validation set and could also predict, to a certain extent, the long-term efficacy of acupuncture at the 12-week follow-up. The gender, subtype of disease, and education level were finally identified as the critical predicting features.

Conclusion: Based on the SVM algorithm and routine clinical features, this study established the models to predict acupuncture efficacy for FD patients. The prediction models developed accordingly are promising to assist doctors in judging patients' responses to acupuncture in advance, so that they could tailor and adjust acupuncture treatment plans for different patients in a prospective rather than the reactive manner, which could greatly improve the clinical efficacy of acupuncture treatment for FD and save medical expenditures.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00271-8.

Nonfuctional pituitary neuroendocrine tumor (NF-PitNET) is highly heterogeneous and generally considered a common intracranial tumor. A series of molecules are involved in NF-PitNET pathogenesis that alter in multiple levels of genome, transcriptome, proteome, and metabolome, and those molecules mutually interact to form dynamically associated molecular-network systems. This article reviewed signaling pathway alterations in NF-PitNET based on the analyses of the genome, transcriptome, proteome, and metabolome, and emphasized signaling pathway network alterations based on the integrative omics, including calcium signaling pathway, cGMP-PKG signaling pathway, mTOR signaling pathway, PI3K/AKT signaling pathway, MAPK (mitogen-activated protein kinase) signaling pathway, oxidative stress response, mitochondrial dysfunction, and cell cycle dysregulation, and those signaling pathway networks are important for NF-PitNET formation and progression. Especially, this review article emphasized the altered signaling pathways and their key molecules related to NF-PitNET invasiveness and aggressiveness that are challenging clinical problems. Furthermore, the currently used medication and potential therapeutic agents that target these important signaling pathway networks are also summarized. These signaling pathway network changes offer important resources for insights into molecular mechanisms, discovery of effective biomarkers, and therapeutic targets for patient stratification, predictive diagnosis, prognostic assessment, and targeted therapy of NF-PitNET.

Background: Short and long sleep durations are common behaviors that could predict several cardiovascular diseases. However, the association between sleep duration and atrial fibrillation (AF) risk is not well-established. AF is preventable, and risk prevention approaches could reduce its occurrence. Investigating whether sleep duration could predict AF incidence for possible preventive interventions and determining the impact of various lifestyle and clinical characteristics on this association to personalize such interventions are essential. Herein, we investigated the association between sleep duration and AF risk using a prospective cohort study and a meta-analysis of epidemiological evidence.

Methods: Data of 6898 people, aged 30-84 years, from the Suita Study, were analyzed. AF was diagnosed during the follow-up by ECG, medical records, checkups, and death certificates, while a baseline questionnaire was used to assess sleep duration. The Cox regression was used to compute the hazard ratios (HRs) and 95% confidence intervals (CIs) of AF risk for daily sleep ≤ 6 (short sleep), ≥ 8 (long sleep), and irregular sleep, including night-shift work compared with 7 h (moderate sleep). Then, we combined our results with those from other eligible prospective cohort studies in two meta-analyses for the short and long sleep.

Results: In the Suita Study, within a median follow-up period of 14.5 years, short and irregular sleep, but not long sleep, were associated with the increased risk of AF in the age- and sex-adjusted models: HRs (95% CIs) = 1.36 (1.03, 1.80) and 1.62 (1.16, 2.26) and the multivariable-adjusted models: HRs (95% CIs) = 1.34 (1.01, 1.77) and 1.63 (1.16, 2.30), respectively. The significant associations between short and irregular sleep and AF risk remained consistent across different ages, sex, smoking, and drinking groups. However, they were attenuated among overweight and hypertensive participants. In the meta-analyses, short and long sleep durations were associated with AF risk: pooled HRs (95% CIs) = 1.21 (1.02, 1.42) and 1.18 (1.03, 1.35). No signs of significant heterogeneity across studies or publication bias were detected.

Conclusion: Short, long, and irregular sleep could be associated with increased AF risk. In the context of predictive, preventive, and personalized medicine, sleep duration should be considered in future AF risk scores to stratify the general population for potential personalized lifestyle modification interventions. Sleep management services should be considered for AF risk prevention, and these services should be individualized according to clinical characteristics and lifestyle factors.

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Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00275-4.