Epma Journal最新文献

{"title":"Anti-prostate cancer protection and therapy in the framework of predictive, preventive and personalised medicine - comprehensive effects of phytochemicals in primary, secondary and tertiary care.","authors":"Alena Mazurakova,&nbsp;Marek Samec,&nbsp;Lenka Koklesova,&nbsp;Kamil Biringer,&nbsp;Erik Kudela,&nbsp;Raghad Khalid Al-Ishaq,&nbsp;Martin Pec,&nbsp;Frank A Giordano,&nbsp;Dietrich Büsselberg,&nbsp;Peter Kubatka,&nbsp;Olga Golubnitschaja","doi":"10.1007/s13167-022-00288-z","DOIUrl":"https://doi.org/10.1007/s13167-022-00288-z","url":null,"abstract":"<p><p>According to the GLOBOCAN 2020, prostate cancer (PCa) is the most often diagnosed male cancer in 112 countries and the leading cancer-related death in 48 countries. Moreover, PCa incidence permanently increases in adolescents and young adults. Also, the rates of metastasising PCa continuously grow up in young populations. Corresponding socio-economic burden is enormous: PCa treatment costs increase more rapidly than for any other cancer. In order to reverse current trends in exploding PCa cases and treatment costs, pragmatic decisions should be made, in favour of advanced populational screening programmes and effective anti-PCa protection at the level of the health-to-disease transition (sub-optimal health conditions) demonstrating the highest cost-efficacy of treatments. For doing this, the paradigm change from reactive treatments of the clinically manifested PCa to the predictive approach and personalised prevention is essential. Phytochemicals are associated with potent anti-cancer activity targeting each stage of carcinogenesis including cell apoptosis and proliferation, cancer invasiveness and metastatic disease. For example, their positive effects are demonstrated for stabilising and restoring mitochondrial health quality, which if compromised is strongly associated with sub-optimal health conditions and strong predisposition to aggressive PCa sub-types. Further, phytochemicals significantly enhance response of cancer cells to anti-cancer therapies including radio- and chemotherapy. Evident plant-based mitigation of negative side-effects frequently observed for conventional anti-cancer therapies has been reported. Finally, dual anti-cancer and anti-viral effects of phytochemicals such as these of silibinin have been demonstrated as being highly relevant for improved PCa management at the level of secondary and tertiary care, for example, under pandemic conditions, since PCa-affected individuals per evidence are highly vulnerable towards COVID-19 infection. Here, we present a comprehensive data analysis towards clinically relevant anti-cancer effects of phytochemicals to be considered for personalised anti-PCa protection in primary care as well as for an advanced disease management at the level of secondary and tertiary care in the framework of predictive, preventive and personalised medicine.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9263437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40584874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metagenomic analysis of the microbiome of the upper reproductive tract: combating ovarian cancer through predictive, preventive, and personalized medicine.","authors":"Xu Qin,&nbsp;Jianglin Zhou,&nbsp;Zizhuo Wang,&nbsp;Chenzhao Feng,&nbsp;Junpeng Fan,&nbsp;Jia Huang,&nbsp;Dianxing Hu,&nbsp;Babak Baban,&nbsp;Shengqi Wang,&nbsp;Ding Ma,&nbsp;Chaoyang Sun,&nbsp;Zhe Zhou,&nbsp;Gang Chen","doi":"10.1007/s13167-022-00286-1","DOIUrl":"https://doi.org/10.1007/s13167-022-00286-1","url":null,"abstract":"<p><strong>Purpose: </strong>We investigated whether ovarian cancer could alter the genital microbiota in a specific way with clinical values. Furthermore, we proposed how such changes could be envisioned in a paradigm of predictive, preventive, and personalized medicine (PPPM).</p><p><strong>Methods: </strong>The samples were collected using cotton swabs from the cervical, uterine cavity, fallopian tubes, and ovaries of patients subjected to the surgical procedures for the malignant/benign lesions. All samples were then analyzed by metagenomic shotgun sequencing. The distribution patterns and characteristics of the microbiota in the reproductive tract of subjects were analyzed and were interpreted in relation to the clinical outcomes of the subjects.</p><p><strong>Results: </strong>While the ovarian cancer was able to alter the genital microbiota, the bacteria were the dominant microorganisms in all samples across all cohorts in the study (median 99%). The microbiota of the upper female reproductive tract were mainly from the cervical, identified by low bacterial biomass and high bacterial diversity. Ovarian cancer had a distinct microbiota signature. The tubal ligation affects its microbial distribution. There were no different species on the surface of platinum-sensitive ovarian tissues compared to samples from platinum-resistant patients.</p><p><strong>Conclusion: </strong>The ovarian cancer-induced changes in microbiota magnify the potential of microbiota as a biotherapeutic modality in the treatment of ovarian cancer in this study and very likely for several malignancies and other conditions. Our findings demonstrated, for the first time, that microbiota could be dissected and applied in more specific fashion based on a predictive, preventive, and personalized medicine (PPPM) model in the treatment of ovarian cancer. Utilizing microbiota portfolio in a PPPM system in ovarian cancer would provide a unique opportunity to a clinically intelligent and novel approach in the treatment of ovarian cancer as well as several other conditions and malignancies.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13167-022-00286-1.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9219379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40405877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital biomarkers and sex impacts in Alzheimer's disease management - potential utility for innovative 3P medicine approach.","authors":"Robbert L Harms, Alberto Ferrari, Irene B Meier, Julie Martinkova, Enrico Santus, Nicola Marino, Davide Cirillo, Simona Mellino, Silvina Catuara Solarz, Ioannis Tarnanas, Cassandra Szoeke, Jakub Hort, Alfonso Valencia, Maria Teresa Ferretti, Azizi Seixas, Antonella Santuccione Chadha","doi":"10.1007/s13167-022-00284-3","DOIUrl":"10.1007/s13167-022-00284-3","url":null,"abstract":"<p><p>Digital biomarkers are defined as objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices. Their use has revolutionized clinical research by enabling high-frequency, longitudinal, and sensitive measurements. In the field of neurodegenerative diseases, an example of a digital biomarker-based technology is instrumental activities of daily living (iADL) digital medical application, a predictive biomarker of conversion from mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to dementia due to AD in individuals aged 55 + . Digital biomarkers show promise to transform clinical practice. Nevertheless, their use may be affected by variables such as demographics, genetics, and phenotype. Among these factors, sex is particularly important in Alzheimer's, where men and women present with different symptoms and progression patterns that impact diagnosis. In this study, we explore sex differences in Altoida's digital medical application in a sample of 568 subjects consisting of a clinical dataset (MCI and dementia due to AD) and a healthy population. We found that a biological sex-classifier, built on digital biomarker features captured using Altoida's application, achieved a 75% ROC-AUC (receiver operating characteristic - area under curve) performance in predicting biological sex in healthy individuals, indicating significant differences in neurocognitive performance signatures between males and females. The performance dropped when we applied this classifier to more advanced stages on the AD continuum, including MCI and dementia, suggesting that sex differences might be disease-stage dependent. Our results indicate that neurocognitive performance signatures built on data from digital biomarker features are different between men and women. These results stress the need to integrate traditional approaches to dementia research with digital biomarker technologies and personalized medicine perspectives to achieve more precise predictive diagnostics, targeted prevention, and customized treatment of cognitive decline.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13167-022-00284-3.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40000049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine.","authors":"Yulu Zheng,&nbsp;Zheng Guo,&nbsp;Yanbo Zhang,&nbsp;Jianjing Shang,&nbsp;Leilei Yu,&nbsp;Ping Fu,&nbsp;Yizhi Liu,&nbsp;Xingang Li,&nbsp;Hao Wang,&nbsp;Ling Ren,&nbsp;Wei Zhang,&nbsp;Haifeng Hou,&nbsp;Xuerui Tan,&nbsp;Wei Wang","doi":"10.1007/s13167-022-00283-4","DOIUrl":"https://doi.org/10.1007/s13167-022-00283-4","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13167-022-00283-4.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10252766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive analysis of spliceosome genes and their mutants across 27 cancer types in 9070 patients: clinically relevant outcomes in the context of 3P medicine.","authors":"Zhen Ye,&nbsp;Aiying Bing,&nbsp;Shulian Zhao,&nbsp;Shuying Yi,&nbsp;Xianquan Zhan","doi":"10.1007/s13167-022-00279-0","DOIUrl":"https://doi.org/10.1007/s13167-022-00279-0","url":null,"abstract":"<p><strong>Relevance: </strong>Spliceosome machinery plays important roles in cell biological processes, and its alterations are significantly associated with cancer pathophysiological processes and contribute to the entire healthcare process in the framework of predictive, preventive, and personalized medicine (PPPM/3P medicine).</p><p><strong>Purpose: </strong>To understand the expression and mutant status of spliceosome genes (SGs) in common malignant tumors and their relationship with clinical characteristics, a pan-cancer analysis of these SGs was performed across 27 cancer types in 9070 patients to discover biomarkers for cancer early diagnosis and prognostic assessment, effectively stratify patients, and improve the survival and prognosis of patients in 3P medical practice.</p><p><strong>Methods: </strong>A total of 150 SGs were collected from the KEGG database. The Python and R language were combined to process the transcriptional data of SGs and clinical data of 27 cancer types in The Cancer Genome Atlas (TCGA) database. Mutations of SGs in 27 cancer types were analyzed to identify the most common mutated SGs, as well as survival-related SGs. Different SGs were screened out, and SGs with survival significance in different types of tumors were found. Furthermore, TCGA and GTEx datasets were used to further confirm the expressions of SGs in different tumors. Western blot assay was performed to verify the expression of SNRPB protein in colon cancer and lung adenocarcinoma. Three SGs were screened out to establish the Bagging model for tumor diagnosis.</p><p><strong>Results: </strong>Among 150 SGs, THOC2, PRPF8, SNRNP200, and SF3B1 had the highest mutation rate. The survival time of mutant THOC2 and SF3B1 was better than that of wild type, respectively. The differential expression analysis of 150 SGs between 674 normal tissue samples and 9,163 tumor tissue samples with 27 cancer types of 9070 patients showed that 13 SGs were highly expressed and 1 was low-expressed. For all cancer types, the prognosis (survival time) of the low-expression group of three SGs (SNRPB, LSM7, and HNRNPCL1) was better than the high expression group, respectively (<i>p</i> < 0.05). Cox hazards model showed that male, over 60 years old, clinical stages III-IV, and with highly expressed SNRPB and HNRNPCL1 had a poor prognosis. GEPIA2 website analysis showed that SNRPB and LSM7 were highly expressed in most tumors but not in LAML, showing low expression. Compared with the control group, the expression of SNRPB protein in colon cancer was increased by Western blot (<i>p</i> < 0.05). Enrichment analysis showed that the differential SGs were mainly enriched in RNA splicing and binding. The average error of 10-fold cross-validation of the Bagging model for diagnosed cancer was 0.093, which demonstrates that the Bagging model can effectively diagnose cancer with a small error rate.</p><p><strong>Conclusions: </strong>This study provided the first landscape of spliceosome c","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203615/pdf/13167_2022_Article_279.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9557340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycomic biomarkers are instrumental for suboptimal health status management in the context of predictive, preventive, and personalized medicine.","authors":"Xiaoni Meng,&nbsp;Biyan Wang,&nbsp;Xizhu Xu,&nbsp;Manshu Song,&nbsp;Haifeng Hou,&nbsp;Wei Wang,&nbsp;Youxin Wang","doi":"10.1007/s13167-022-00278-1","DOIUrl":"https://doi.org/10.1007/s13167-022-00278-1","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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).</p><p><strong>Conclusions: </strong>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13167-022-00278-1.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203619/pdf/13167_2022_Article_278.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9557341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing a preimplantation proteomic approach to advance assisted reproduction technologies in the framework of predictive, preventive, and personalized medicine.","authors":"Vasiliki Kanaka, Stavros Proikakis, Petros Drakakis, Dimitrios Loutradis, George Th Tsangaris","doi":"10.1007/s13167-022-00282-5","DOIUrl":"10.1007/s13167-022-00282-5","url":null,"abstract":"<p><p>The evolution of the field of assisted reproduction technology (ART) in the last 40 years has significantly contributed to the management of global infertility. Despite the great numbers of live births that have been achieved through ART, there is still potential for increasing the success rates. As a result, there is a need to create optimum conditions in order to increase ART efficacy. The selection of the best sperm, oocyte, and embryo, as well as the achievement of optimal endometrial receptivity, through the contribution of new diagnostic and treatment methods, based on a personalized proteomic approach, may assist in the attainment of this goal. Proteomics represent a powerful new technological development, which seeks for protein biomarkers in human tissues. These biomarkers may aid to predict the outcome, prevent failure, and monitor in a personalized manner in vitro fertilization (IVF) cycles. In this review, we will present data from studies that have been conducted in the search for such biomarkers in order to identify proteins related to good sperm, oocyte, and embryo quality, as well as optimal endometrial receptivity, which may later lead to greater results and the desirable ART outcome.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40000050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive factors, preventive implications, and personalized surgical strategies for bone metastasis from lung cancer: population-based approach with a comprehensive cancer center-based study.","authors":"Xianglin Hu,&nbsp;Wending Huang,&nbsp;Zhengwang Sun,&nbsp;Hui Ye,&nbsp;Kwong Man,&nbsp;Qifeng Wang,&nbsp;Yangbai Sun,&nbsp;Wangjun Yan","doi":"10.1007/s13167-022-00270-9","DOIUrl":"https://doi.org/10.1007/s13167-022-00270-9","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>Two independent study cohorts including LC-BM patients were analyzed, including the Surveillance, Epidemiology, and End Results (SEER) cohort (<i>n</i> = 203942) and the prospective Fudan University Shanghai Cancer Center (FUSCC) cohort (<i>n</i> = 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Graphical abstract: </strong></p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13167-022-00270-9.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897531/pdf/13167_2022_Article_270.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10813476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and validation of a transcriptomic signature-based model as the predictive, preventive, and personalized medical strategy for preterm birth within 7 days in threatened preterm labor women.","authors":"Yuxin Ran,&nbsp;Jie He,&nbsp;Wei Peng,&nbsp;Zheng Liu,&nbsp;Youwen Mei,&nbsp;Yunqian Zhou,&nbsp;Nanlin Yin,&nbsp;Hongbo Qi","doi":"10.1007/s13167-021-00268-9","DOIUrl":"https://doi.org/10.1007/s13167-021-00268-9","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897543/pdf/13167_2021_Article_268.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10819840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional metabolome profiling may improve individual outcomes in colorectal cancer management implementing concepts of predictive, preventive, and personalized medical approach.","authors":"Yu Yuan,&nbsp;Chenxin Yang,&nbsp;Yingzhi Wang,&nbsp;Mingming Sun,&nbsp;Chenghao Bi,&nbsp;Sitong Sun,&nbsp;Guijiang Sun,&nbsp;Jingpeng Hao,&nbsp;Lingling Li,&nbsp;Changliang Shan,&nbsp;Shuai Zhang,&nbsp;Yubo Li","doi":"10.1007/s13167-021-00269-8","DOIUrl":"https://doi.org/10.1007/s13167-021-00269-8","url":null,"abstract":"<p><strong>Objectives: </strong>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).</p><p><strong>Methods: </strong>In this study, CRC patients (<i>n</i> = 30) and HC (<i>n</i> = 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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.<b>Clinical trials registration number</b>: ChiCTR2000039410.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s13167-021-00269-8.</p>","PeriodicalId":54292,"journal":{"name":"Epma Journal","volume":null,"pages":null},"PeriodicalIF":6.5,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897532/pdf/13167_2021_Article_269.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10819839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}