The aim of this study was to integrate proteomic and metabolomic datasets derived from tissue and serum samples of patients with endometrial cancer (EC) to comprehensively characterize the molecular landscape associated with the disease. A total of 60 patients with EC and 32 healthy controls were included. Tissue and serum samples were analyzed using data-independent acquisition proteomics together with metabolomics platforms. Molecular characteristics were investigated through differential analyses followed by pathway enrichment evaluation. In addition, machine learning approaches were applied to identify serum-derived biomarkers and to develop predictive diagnostic models. A total of 8,542 proteins and 576 metabolites were identified in tissue samples, whereas 465 proteins and 405 metabolites were identified in serum samples. Tissue-level analysis demonstrated predominant enrichment in oncogenic signaling pathways, including the RAS-RAF-MAPK cascade, along with metabolic pathways associated with amino acid and nucleotide metabolism. In contrast, serum profiles reflected broader systemic alterations, particularly involving immune-related processes, platelet activation, complement cascade pathways, and metabolism of branched-chain amino acids. Integration of serum multi-omics data identified CFHR3, LAMP2, L-valine, and L-isoleucine as candidate biomarkers. The multi-omics combined predictive model achieved an average area under the curve of 0.98, demonstrating substantially improved performance compared with models based on individual proteins or metabolites. Distinct molecular characteristics were observed between EC tissue and serum. The serum-based multi-omics predictive model demonstrated strong diagnostic performance and identified a candidate biomarker panel with potential utility for non-invasive detection of EC.

Background and Objective:Acute coronary syndrome (ACS) is a common cardiovascular disease in clinical practice. It is caused mainly by vulnerable plaque rupture (PR) or surface plaque erosion (PE) caused by serious thrombotic events, and eventually leads to myocardial blood supply insufficiency or necrosis. The disease has high morbidity and mortality rates. In this study, we review the literature on biomarkers of ACS metabolites and modification of disease by altering related metabolic pathways through drugs, aiming to provide clarity on potential biomarkers of disease identified to date. Methods:PubMed was used for literature review. From January 1, 2014 to December 3, 2024, English articles on clinical trials, randomized controlled trials of metabolomics studies in ACS were included. Key Content and Findings:In this review, we discuss the advantages and disadvantages of three techniques currently used for metabolomic analysis. In addition, the recent decade of metabolomic approaches to the discovery of potential diagnostic and prognostic biomarkers for ACS is reviewed. It was found that the metabolites changed in patients with ACS were mostly amino acids, lipids and carbohydrates. Tryptophan and glutamine can be used as potential diagnostic biomarkers. Mannitol and ceramide can be used as prognostic biomarkers. Drugs can improve disease by affecting changes in metabolites in the body. Conclusions:ACS studies based on metabolomics have demonstrated great potential for identifying disease-related metabolomic features in the discovery of potential biomarkers for diagnosis and prognosis and mechanisms of drug therapy.

Bladder, kidney, and prostate cancers are prevalent urinary cancers, and developing efficient detection methods is of significance for the early diagnosis of them. However, noninvasive and sensitive detection of urinary cancers still challenges traditional techniques. In this study, we developed a SERS-based method to analyze serum samples from patients with urinary cancers. Rapid, label-free, and highly sensitive detection of human sera is achieved by cleaning and aggregating silver nanoparticles. Furthermore, a long short-term memory deep learning algorithm is used to distinguish serum spectra, and the performance of the model is evaluated by comparing the accuracy, sensitivity, specificity, and receiver operating characteristic curves. Taking advantage of SERS and machine learning in sensitivity and data processing, the three urinary cancers are clearly classified. This is the first attempt to exploit the SERS-machine learning strategy to discriminate multiple urinary cancers with clinical serum samples, and our results showed the potential application of this method in the early diagnosis and screening of cancers.

BackgroundAcute coronary syndrome (ACS) is a cardiovascular disease caused by acute myocardial ischemia. The aim of this study was to use urine metabolomics to explore potential biomarkers for the diagnosis of ACS and the changes in metabolites during the development of this disease.MethodsUrine samples were collected from 81 healthy controls and 130 ACS patients (103 UA and 27 AMI). Metabolomics based on liquid chromatography-mass spectrometry (LC-MS) was used to analyze urine samples. Statistical analysis and functional annotation were applied to identify potential metabolite panels and altered metabolic pathways between ACS patients and healthy controls, unstable angina (UA), and acute myocardial infarction (AMI) patients.ResultsThere were significant differences in metabolic profiles among the UA, AMI and control groups. A total of 512 differential metabolites were identified in this study. Functional annotation revealed that changes in arginine biosynthesis, cysteine and methionine metabolism, galactose metabolism, sulfur metabolism and steroid hormone biosynthesis pathways occur in ACS. In addition, a panel composed of guanidineacetic acid, S-adenosylmethionine, oxindole was able to distinguish ACS patients from healthy controls. The AUC values were 0.8339 (UA VS HCs) and 0.8617 (AMI VS HCs). Moreover, DL-homocystine has the ability to distinguish between UA and AMI, and the area under the ROC curve is 0.8789. The metabolites whose levels increased with disease severity the disease were involved mainly in cysteine and methionine metabolism and the galactose metabolism pathway. Metabolites that decrease with disease severity are related mainly to tryptophan metabolism.ConclusionThe results of this study suggest that urinary metabolomics studies can reveal differences between ACS patients and healthy controls, which may help in understanding its mechanisms and the discovery of related biomarkers.

BackgroundBladder cancer is a common malignant tumor of the urinary system. The progression of the condition is associated with a poor prognosis, so it is necessary to identify new biomarkers to improve the diagnostic rate of bladder cancer.MethodsIn this study, 338 urine samples (144 bladder cancer, 123 healthy control, 32 cystitis, and 39 upper urinary tract cancer samples) were collected, among which 238 samples (discovery group) were analyzed by LC−MS. The urinary proteome characteristics of each group were compared with those of bladder cancer, and the differential proteins were defined by bioinformatics analysis. The pathways and functional enrichments were annotated. The selected proteins with the highest AUC score were used to construct a diagnostic panel. One hundred samples (validation group) were used to test the effect of the panel by ELISA.ResultsCompared with the healthy control, cystitis and upper urinary tract cancer samples, the number of differential proteins in the bladder cancer samples was 325, 158 and 473, respectively. The differentially expressed proteins were mainly related to lipid metabolism and iron metabolism and were involved in the proliferation, metabolism and necrosis of bladder cancer cells. The AUC of the panel of APOL1 and ITIH3 was 0.96 in the discovery group. ELISA detection showed an AUC of 0.92 in the validation group.ConclusionThis study showed that urinary proteins can reflect the pathophysiological changes in bladder cancer and that important molecules can be used as biomarkers for bladder cancer screening. These findings will benefit the application of the urine proteome in clinical research.

Sevoflurane (Sevo), a commonly used inhalant anesthetic clinically, is associated with a worsened cancer prognosis, and we investigated its effect on RNA methylase tRNA aspartic acid methyltransferase 1 (TRDMT1) expression and ovarian cancer (OC) cell malignant phenotypes. Human OC cells (OVCAR3/SKOV3) were pretreated with 3.6

Acute cerebral infarction(ACI)has the characteristics of onset nasty and high mortality,and thus the rapid determination of the occurrence and development of ACI plays a key role in the diagnosis,treatment and prognosis of ACI patients.It has shown that the serum level of human haptoglobin(Hp)is related to ACI.In this study,surface enhanced Raman scattering(SERS)combined with immune recognition was applied to establish a quantitative analysis method for serum Hp.Firstly,the SERS substrate of silver nanoparticles was prepared on silicon wafer,and 4-mercaptobenzoic Acid(MBA)was used as a Raman probe by forming Ag—S bond and connecting it on the surface of nanoparticles.The carboxyl group of MBA was linked to amino group of self-made high-affinity antibody through forming CO—NH structure thus forming a SERS self-assembled chip of Hp(Ag/MBA/anti-Hp).Hp in serum could be specifically captured by antibodies on SERS substrate,which caused the shift of SERS characteristic peak of MBA.The results showed that there was a good linear relationship between the logarithm of Hp concentration and the SERS characteristic peak shift of MBA.The detection range was 1-1000 ng/mL(R2=0.988).The Hp concentrations in serum of 90 ACI patients were determined by this method,and the results were consistent with those of ELISA method,which proved the practicability and accuracy of this method.This method was highly specific,simple and convenient,which could realize the specific recognition and quantitative analysis of serum Hp,so as to be an effective means for clinical detection of serum Hp,thus providing a reference for the treatment and prognosis of ACI.

The redox state of the thiol groups of protein cysteine residues is closely related to the local redox level of cells. When these thiol groups are oxidized or reduced, they can greatly affect protein structure, thereby modulating their biological functions and eventually affecting the biological processes and cell fate. In this study, a strategy aiming at selectively labeling the free thiol group of protein cysteine was proposed. In this method, N-ethylmaleimide (NEM), a thiol reactive reagent, was used to block the free thiol groups on the proteins prior to the routine sample processing in proteomics flow ( Reduction of disulfide bond by dithiothreitol, alkylation blockage byiodoacetamide). Therefore, the original free thiol groups, as well as those generated from the reduction treatment by dithiothreitol, were blocked with two different thiol reactive reagents with different molecular weights, leading to specific identification of the original free thiol groups within proteins. By using this strategy, a proteomic investigation was performed on the free thiol groups of mitochondrial proteins in colorectal cancer tissues. A total of 1549 mitochondrial proteins were identified, including protein disulfide-isomerase A3, peroxiredoxin-1, mitochondrial NADH dehydrogenase [ ubiquinone ] flavoprotein 2, mitochondrial inner membrane protein, mitochondrial acetyl-CoA acyltransferase, malate dehydrogenase, calnexin, mitochondrial aspartate aminotransferase, mitochondrial succinate dehydrogenase [ ubiquinone ] iron-sulfur subunit, etc. Specially, 348 peptides containing free sulfhydryl groups were identified, belonging to 253 proteins. The proteomics data of the mitochondrial proteins as well as the peptides containing free thiols in colon cancer tissues could provide new ideas for further investigation on the redox targets as well as novel biomarkers in mitochondrial proteins in colon cancer.

Glutamate decarboxylase antibody (GADA) and insulin autoantibody (IAA) are essential biomarkers for type 1 diabetes mellitus (T1DM). These islet autoantibodies are present in the serum of patients and are useful for the early diagnosis of T1DM. In this study, we developed a surface-enhanced Raman scattering (SERS)-based immunoassay for the detection of two T1DM-related autoantibodies, GADA and IAA. GADA and IAA are specifically captured by their antigens immobilized on the mercaptobenzoic acid (MBA)-functionalized silver substrates. They can be quantitatively detected based on the Raman frequency shifts of the cross-linker MBA. Combined with a 96-well plate, the immunoassay enabled a high-throughput analysis of the two biomarkers. The results suggest that the proposed method has significant potential for clinical application in rapid, sensitive, and high-throughput screening of T1DM.

Abstract Objectives Iron metabolism-related genes (IMRGs) play important roles in the prognostic assessment of many tumours. However, IMRGs have not been reported as prognostic biomarkers in bladder urothelial carcinoma (BLCA). Methods Gene expression profiles and clinical data from BLCA patients were obtained from The Cancer Genome Atlas (TCGA) database. We used the DESeq2 package to screen for differentially expressed genes (DEGs). The predictive values of the differentially expressed IMRGs in BLCA patients were further evaluated using univariate Cox regression analysis. The risk-scoring model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm. The performance of this model for predicting the prognosis of BLCA patients in TCGA-BLCA cohort was assessed using Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curves. This risk-scoring model was combined with the clinicopathological characteristics of BLCA patients in a multiple regression analysis, and a nomogram was constructed using the independent predictors identified. ROC analysis and calibration curves were adapted to test the predictive ability of the nomogram. Gene set enrichment analysis (GSEA) was used to identify potential molecular pathways and processes enriched by differential expression genes between risk groups. Finally, we explored the ability of the risk-scoring model to assess immune cell infiltration levels through a correlation analysis. Results Fourteen identified IMRGs with prognostic value were incorporated into the risk-scoring model. The ROC and K–M survival curves indicated that the model could effectively predict the overall survival (OS) outcomes of BLCA patients. The multiple regression analysis revealed that the risk-scoring model could be used as an independent prognostic factor for BLCA patients, and the associated nomogram could effectively predict the OS outcomes of BLCA patients. GSEA revealed that the DEGs between the risk groups were mainly involved in biological processes such as developmental process, cell cycle, mitosis, RHO GTPase reaction, DNA repair, and extracellular matrix regulation. The immune infiltration analysis showed that the infiltration levels of immune cells such as natural killer cells, memory T cells, effector T cells, Th2 cells, and macrophages differed significantly between the risk groups. Conclusions IMRGs screening revealed prognosis-associated genes. The prognostic model constructed could effectively predict the prognosis of BLCA patients, and the identified genes represent potential targets for BLCA treatment.

结直肠癌是人类第三大常见癌症，约占所有癌症的10%,也是癌症相关死亡的第二大原因。2020年全球约有190万新发病例和90万死亡病例 [1] 。近年来，随着经济发展以及生活方式和饮食的改变，中国结直肠癌的发病率和死亡率增加 [2] 。这给医疗系统带来了沉重的经济负担。因此，识别和评估新的生物标志物和治疗靶点迫在眉睫。目前，蛋白酶体26S非ATP酶调节亚基7（PSMD7）的表达及其在结直肠癌进展中的作用在很大程度上仍然未知。

An aptamer-based electrochemical sensor was assembled to detect glioma cells (GC). In order to improve the detection of low concentration GC by the sensor, DNA walker was used in the setting of the sensor. The sensor can distinguish low concentration GC through the catalytic reaction caused by DNA walker. After optimizing different parameters, the proposed sensor can achieve linear detection of 10-10,000 cells/mL. The detection limit can reach 5 cell/mL. The specificity, reproducibility and stability of the biosensor were investigated. The results show that the biosensor can detect the target cells specifically and has good reproducibility. The biosensor can be stored for a long time and has no obvious effect on the detection results. Finally, the clinical application ability of biosensor was preliminarily studied by recycling test. The recovery rate and RSD of the biosensor for different concentrations of GC are within the acceptable range, which proves that the biosensor has the potential to detect clinical samples.

In diagnosing prostate cancer and distinguishing it from other prostate diseases, the ratio of the concentration of free prostate-specific antigen (f-PSA) to total prostate-specific antigen (t-PSA), i.e., (f-PSA%) is more accurate than the concentration of t-PSA alone. Immunoassay based on surface-enhanced Raman scattering (SERS) frequency shift has been proven to be particularly suitable for detecting large biomolecules with high reproducibility. Along similar lines, the present study developed a SERS-based biosensor that simultaneously detects t-PSA and f-PSA. The 4-mercaptobenzoic acid (MBA) on the immunocapture substrate is coupled to the t-PSA antibody through the carboxyl group, and the combination of t-PSA induces the Raman frequency shifts of MBA. The immunocolloidal gold attached with f-PSA antibodies selectively capture the f-PSA that immobilized on the MBA-modified SERS substrates, allowing for f-PSA quantification according to the SERS intensities of the 5, 5'-Dithiobis (succinimidyl-2-nitrobenzoate) (DSNB) probe. The results show that f-PSA and t-PSA have good linear response in the concentration scale of 0.1-20 ng/mL, and 1-200 ng/mL, respectively. The biosensor combines Raman frequency shifts and intensities, which greatly simplifies traditional procedures for f-PSA% detection. All the results demonstrated the great potential of the proposed biosensor in highly reproducible and accurate diagnosis of prostate cancers.

结直肠癌（CRC）是全球最普遍和最致命的实体恶性肿瘤之一 [1] ,也是全球癌症相关死亡的第二大常见原因，终生风险约为4%至5% [2] 。CRC的发病率和死亡率正在迅速增加 [3] 。本实验通过二维色谱质谱联用技术和免疫印迹探究大鼠LIM半胱氨酸丰富域蛋白1（LMCD1）在癌组织和癌旁组织中的差异表达及意义，报道如下。

近年来结直肠癌已成为新发癌症病例发病率第三高和癌症死亡原因第三高的原因，而中国是结直肠癌年均新发病例数和死亡病例数第一的国家 [1-2] 。PLOD1（前胶原赖氨酸2-氧代戊二酸5-双加氧酶1）是编码赖氨酸羟化酶LH1的基因，该基因存在于染色体1p36，包含19个外显子 [3-4] 。赖氨酰羟化酶是一种膜结合的同源二聚体蛋白，定位于内质网的蓄水池，可以催化赖氨酰残基羟基化，所得的羟赖氨酸基团是胶原蛋白中碳水化合物的附着位点，对分子间交联的稳定性至关重要 [5] 。本实验通过二维色谱质谱联用技术以及免疫印迹试验，分析PLOD1在结直肠癌组织及癌旁组织中的表达情况，旨在探究其与结直肠癌发生、发展之间的关系。

Sensitive and multiple detection of the biomarkers of type 1 diabetes mellitus (T1DM) is vital to the early diagnosis and clinical treatment of T1DM. Herein, we developed a SERS-based biosensor using polyvinylidene fluoride (PVDF) membranes as a flexible support for the detection of glutamic acid decarboxylase antibodies (GADA) and insulin autoantibodies (IAA). Two kinds of silver-gold core-shell nanotags embedded with Raman probes and attached with GADA or IAA antibodies were synthesized to capture the targets, enabling highly sensitive and highly selective detection of GADA and IAA. The embedded Raman probes sandwiched between silver and gold layers guaranteed spectral stability and reliability. Moreover, the utilization of two Raman probes enables simultaneous and multiplexing detection of both GADA and IAA, improving the detection accuracy for T1DM. The proposed SERS-based method has been proven feasible for clinical sample detection, demonstrating its great potential in sensitive, reliable, and rapid diagnosis of T1DM.

Background and aims: Visceral adiposity index (VAI), an indicator of visceral fat, is associated with metabolic health and arterial stiffness. However, studies correlating VAI and stroke are limited. This study aimed to explore the association between VAI and incident stroke in the Chinese population. Methods and results: We retrospectively analysed the data of 9127 individuals enrolled in the China Health and Retirement Longitudinal Study. The first survey of the study was conducted during 2011e2012 and the individuals were followed up until Survey 4 ( 2017-2018). Multivariable-adjusted Cox regression models were used to evaluate the association between VAI and stroke. The mean age of the study population was 59.3 +/- 9.5 years and 4938 (54.1%) participants were women. During the median follow-up of 5.2 [1.0-7.0] years, 833 (9.1%) participants developed stroke, and the cumulative incidence of stroke increased with increasing quartiles of VAI (8.6%, 8.7%, 9.2%, and 10.0%). Compared to those in the first quartile of VAI, individuals in the fourth quartile had an increased risk of stroke (adjusted hazard ratio, 1.45; 95% CI, 1.15-1.75). The results were stable in several sensitivity analyses. Conclusion: Our findings suggest a positive association between VAI and incident stroke in the Chinese population. (C) 2022 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

Background and aims: Psychological symptoms are prevalent among individuals with non-communicable diseases, while the longitudinal association between triglyceride glucose (TyG) index, an indicator of metabolic health, and depression progression remains unclear yet. This study aims to investigate the association of baseline TyG index and depression progression in middle-aged and elder adults. Methods and results: This retrospective cohort study enrolled 8287 participants aged 45 years or above from national China Health and Retirement Longitudinal Study in visit 1 (2011-2012), which were biennially followed for depression score until visit 4 (2017-2018). Multivariateadjusted regression models were used to evaluate the association of baseline TyG index with the individual level change rate and slope of depression score. The mean age (+/- SD) of participants was 58.25 +/- 9.10 years, and 3806 (45.9%) were men. There was no significant difference of depression score at baseline across TyG quartile groups (P Z 0.228). Participants in the highest quartile of TyG index had a 0.124 (95% CI: 0.018-0.230) higher change rate of depression score, and a 0.127 (95% CI: 0.019-0.235) higher change slope, compared to those in the lowest. The observed associations were consistent in multiple sensitivity analyses, and stable in men, the elder, and overweight people. Conclusion: TyG index is positively associated with depression progression especially in men, the elder and overweight people, which provides new insights for the primary prevention of depression disorder. (c) 2022 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

结直肠癌的发病率在发达国家位列第三，死亡人数在男性排行中最多的是肺癌、前列腺癌和结直肠癌，女性为肺癌、乳腺癌和结直肠癌 [1] 。生活方式导致年轻化趋势且患病率不断增加 [2] ，超过90%的胃肠道癌症是由饮食习惯引起的。我们实验室运用液相色谱-质谱联用技术，通过对10名结直肠癌患者的肿瘤组织与癌旁正常组织进行蛋白质组学分析，实验结果显示出差异表达的蛋白质，

Understanding the structure-activity correlation and reaction mechanism of the catalytic process in an acetic acid-sodium acetate (HAc-NaAc) buffer environment is crucial for the design of efficient nanozymes. Here, we first reported a lattice restructuration of Au-LaNiO3-δ nanofibers (NFs) after acidification with the HAc-NaAc buffer to show a significantly enhanced oxidase-like property. Surface-enhanced Raman spectroscopy (SERS) and density functional theory (DFT) calculation confirm the direct evidence for the formation of specific enhanced intermediate O-O species after acidification, indicating that the insertion of the carboxyl group in the A-Au/LaNiO3-δ NFs plays crucial roles in both producing vacancies in HAc-NaAc solution from its dissociation during the catalytic process and the protection of the vacancies, which can be directly interacted with oxygen in the environment to produce O-O species, realizing the enhanced oxidation of substrate molecules. The insertion of the carboxyl group increased the oxidase-like catalytic activity by 2.38 times and the SERS activity by 5.27 times. This strategy offers a way to construct an efficient nanozyme-linked immunosorbent assay system for the diagnosis of cancer through the highly sensitive SERS identification of exosomes.