Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study.

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer.

BACKGROUND: Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC). METHODS: A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC. RESULTS: We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816-0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716-0.805). CONCLUSIONS: These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.

Changes of serum metabolites levels during neoadjuvant chemoradiation and prediction of the pathological response in locally advanced rectal cancer.

INTRODUCTION: Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT. OBJECTIVES: This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients. METHODS: Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models. RESULTS: Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively. CONCLUSION: This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.

UBE3A alleviates isoproterenol-induced cardiac hypertrophy through the inhibition of the TLR4/MMP-9 signaling pathway.

Cardiac hypertrophy is considered to be a leading factor in heart function-related deaths. In this study, we explored the potential mechanism underlying cardiac hypertrophy induced by isoproterenol. Our results showed that isoproterenol induced cardiac hypertrophy in AC16 cells, as reflected by the increased cell surface area and increased hypertrophic markers, which was accompanied by increased ubiquitin-protein ligase E3a (UBE3A) expression. Moreover, UBE3A knockdown by siRNAs accelerated cardiac hypertrophy, suggesting that increased UBE3A expression induced by isoproterenol might be a protective response and UBE3A might be a protective factor against cardiac hypertrophy. Our study also revealed that UBE3A knockdown increased the protein expression of the TLR4/MMP-9 pathway that has been shown to be associated with cardiac hypertrophy, which suggested that UBE3A-mediated protection is likely to be associated with the blockade of the TLR4/MMP-9 signaling pathway. UBE3A might be thus a potential target gene for the treatment of cardiac hypertrophy.

Highly sensitive detection of nucleic acids using a cascade amplification strategy based on exonuclease III-assisted target recycling and conjugated polyelectrolytes.

In this paper, a novel ratiometric and cascade amplification strategy was developed by combining the unique signal amplification and effective fluorescence resonance energy transfer (FRET) property of conjugated polymers with the Exo III-assisted target recycling method. The target DNA (ssDNAc) could be hybridized with the duplex-stranded probe to trigger the cyclic digestion of the probe strands and lead to the continuous release of fluorescein from the probe. The proposed strategy thus shows enhanced sensitivity toward target DNA with a detection limit of 0.38 nM, which is more sensitive than the previously reported comparable biosensors based on conjugated polyelectrolytes. Furthermore, this method exhibited an improved performance to discriminate single mismatched targets through an efficient FRET-based ratiometric detection method using a conjugated polymer as a donor and an optical transducer. More importantly, this cascade amplification approach offers the advantages of simplicity, which avoids multiple utilization of probes and complex assay steps required in traditional amplification methods.

Prognostic model construction and immune microenvironment analysis of esophageal cancer based on gene expression data and microRNA target genes.

Background: Accumulating evidence suggests that microRNA-target genes are closely related to tumorigenesis and progression. This study aims to screen the intersection of differentially expressed mRNAs (DEmRNAs) and the target genes of differentially expressed microRNAs (DEmiRNAs), and to construct a prognostic gene model of esophageal cancer (EC). Methods: Gene expression, microRNA expression, somatic mutation, and clinical information data of EC from The Cancer Genome Atlas (TCGA) database were used. The intersection of DEmRNAs and the target genes of DEmiRNAs predicted by the Targetscan database and microRNA Data Integration Portal (mirDIP) database were screened. The screened genes were used to construct a prognostic model of EC. Then, the molecular and immune signatures of these genes were explored. Finally, the GSE53625 dataset from the Gene Expression Omnibus (GEO) database was further used as a validation cohort to confirm the prognostic value of the genes. Results: Six genes on the grounds of the intersection of DEmiRNAs target genes and DEmRNAs were identified as prognostic genes, including ARHGAP11A, H1.4, HMGB3, LRIG1, PRR11, and COL4A1. Based on the median risk score calculated for these genes, EC patients were divided into a high-risk group (n=72) and a low-risk group (n=72). Survival analysis showed that the high-risk group had a significantly shorter survival time than the low-risk group (TCGA and GEO, P<0.001). The nomogram evaluation showed high reliability in predicting the 1-year, 2-year, and 3-year survival probability of EC patients. Compared to low-risk group, higher expression level of M2 macrophages was found in high-risk group of EC patient (P<0.05), while STAT3 checkpoints showed attenuated expression level in high-risk group. Conclusions: A panel of differential genes was identified as potential EC prognostic biomarkers and showed great clinical significance in EC prognosis.

Highly Stable Core-Shell Structured Semiconducting Polymer Nanoparticles for FRET-Based Intracellular pH Imaging.

Highly stable semiconducting polymer nanoparticles (NPs) (poly(9,9-dioctylfluorenyl-2,7-diyl) (PFO)/ poly(fluorene-2,7-ylenevinylene-co-phenylene) (PFV)-dopamine (DA) NPs) with previously unreported core-shell structure are developed for ratiometric sensing of intracellular pH values. PFO/PFV-DA NPs comprise central polyfluorene (PFO) as donor and PFV as acceptor, in which the donor and acceptor are spatially separated into the central core and nanoparticle shell. Specifically, thick PFV shells can not only significantly minimize the quenching interference of dopamine on the emission of standard reference (PFO), but are also able to maximize its accessibility to pH-sensitive dopamine and lead to sensitive response to pH changes. The resulting core-shell PFO/PFV NPs are structurally and optically stable, which can avoid the photobleaching and leakage of materials issues compared to traditional semiconducting polymer nanoparticles (SPNs)-based fluorescence resonance energy transfer (FRET) systems containing small molecules. Additionally, the designed compact PFO/PFV-DA NPs show quantitative response to the pH values in aqueous media and are capable of mapping intracellular pH fluctuations by ratiometric imaging. This work may open up opportunities for the generalizability of the consistent ratiometric emission intensity strategy based on core-shell structured SPNs nanoprobes for highly sensitive biological sensing.

Two-photon semiconducting polymer nanoparticles as a new platform for imaging of intracellular pH variation.

Intracellular pH (pHi) plays a crucial role in cell physiological and pathological processes. We herein report an efficient pH-sensitive sensor based on two-photon excitable semiconducting polymer nanoparticles (PFV/PSMA-DA NPs) for pHi sensing. PFV/PSMA NPs were functionalized with redox-active dopamine (DA) and the obtained PFV/PSMA-DA NPs showed sensitive and reversible pH response over the pH range of 5.0-9.0. Owning to the high biocompatibility and pH-responsive DA, PFV/PSMA-DA NPs show low cytotoxicity and the quantification and imaging of intracellular pH changes of HeLa cells were successfully realized. Moreover, the detection of intracellular pH fluctuation induced by redox species such as NAC (N-acetylcysteine) and H2O2 was also achieved by both one- and two-photon excitation of the PFV/PSMA-DA NPs probe. This work clearly shows that nanoprobe based on two-photon PFV/PSMA-DA NPs could serve as a promising platform for quantitatively monitoring the intracellular pH fluctuations.

pH/NIR-responsive semiconducting polymer nanoparticles for highly effective photoacoustic image guided chemo-photothermal synergistic therapy.

Multifunctional drug delivery nanoplatform (PDPP3T@PSNiAA NPs) based on NIR absorbing semiconducting polymer nanoparticles for pH/NIR light-controllably regulated drug release has been successfully prepared. In this strategy, pH/thermal-sensitive multifunctional polymer polystyrene-b-poly(N-isopropylacrylamide-co-acrylic acid) (PSNiAA) was meticulously designed and synthesized using the reversible addition fragmentation chain transfer (RAFT) polymerization method. Furthermore, PSNiAA was used to functionalize diketopyrrolopyrrole-based semiconducting polymer (PDPP3T) to combine photothermal capacity and pH/thermo-responsive drug release in one entity. The prepared PDPP3T@PSNiAA NPs exhibited high photothermal conversion efficiency (η = 34.1%) and excellent photoacoustic (PA) brightness. Meanwhile, benefiting from the photothermal effect of PDPP3T and the pH/thermal-responsive properties of PSNiAA, Dox-loaded PDPP3T@PSNiAA NPs (PDPP3T@PSNiAA-Dox NPs) were able to controllably regulate the release of Dox by pH/NIR light, in which the enhanced drug release at acidic condition upon NIR irradiation phenomenon would minimize unnecessary drug release in normal tissues and was highly beneficial for precise synergistic chemo- and photothermal therapy.

Rapid aptasensor capable of simply detect tumor markers based on conjugated polyelectrolytes.

In this paper, a very simple, easily-operated and universal platform is proposed for tumor marker detection. In this strategy, tumor marker-specific aptamer, which can quench the fluorescence of polyfluorene-based cationic conjugated polyelectrolytes (PFN+), are used as recognizing probes. Upon addition of tumor marker, the aptamer can be assembled into the tumor marker-aptamer complex, resulting in fluorescence recovery of PFN+ and the detection of the targets. The most widely-used tumor markers, carcinoembryonic antigen (CEA) and fetoprotein (AFP) have been chosen as the model analytes for this work. The sensing method is capable of rapidly detect target protein within 5 min without complex handling procedure and expensive instruments. Compared with previous studies, the assay presented here is really simple and avoids either conjugated polyelectrolytes (CPEs) modification or oligonucleotide labeling. This method also shows a wide detection range of 3 orders of magnitude and the detection limit is 0.316 ng/mL for CEA and 1.76 ng/mL for AFP. Furthermore, the approach requires only a convenient"mix-and-detect" procedure and offers a universal platform for the sensitive detection of any target molecule of choice according to the selected aptamer.