IDMIR: identification of dysregulated miRNAs associated with disease based on a miRNA-miRNA interaction network constructed through gene expression data.

Micro ribonucleic acids (miRNAs) play a pivotal role in governing the human transcriptome in various biological phenomena. Hence, the accumulation of miRNA expression dysregulation frequently assumes a noteworthy role in the initiation and progression of complex diseases. However, accurate identification of dysregulated miRNAs still faces challenges at the current stage. Several bioinformatics tools have recently emerged for forecasting the associations between miRNAs and diseases. Nonetheless, the existing reference tools mainly identify the miRNA-disease associations in a general state and fall short of pinpointing dysregulated miRNAs within a specific disease state. Additionally, no studies adequately consider miRNA-miRNA interactions (MMIs) when analyzing the miRNA-disease associations. Here, we introduced a systematic approach, called IDMIR, which enabled the identification of expression dysregulated miRNAs through an MMI network under the gene expression context, where the network's architecture was designed to implicitly connect miRNAs based on their shared biological functions within a particular disease context. The advantage of IDMIR is that it uses gene expression data for the identification of dysregulated miRNAs by analyzing variations in MMIs. We illustrated the excellent predictive power for dysregulated miRNAs of the IDMIR approach through data analysis on breast cancer and bladder urothelial cancer. IDMIR could surpass several existing miRNA-disease association prediction approaches through comparison. We believe the approach complements the deficiencies in predicting miRNA-disease association and may provide new insights and possibilities for diagnosing and treating diseases. The IDMIR approach is now available as a free R package on CRAN (https://CRAN.R-project.org/package=IDMIR).

iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network.

Interactions between Tumor microenvironment (TME) cells shape the unique growth environment, sustaining tumor growth and causing the immune escape of tumor cells. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the identification of cancer-related TME cells. Here, we proposed a novel network-based computational method, named as iATMEcell, to identify the abnormal TME cells associated with the biological outcome of interest based on a cell-cell crosstalk network. In the method, iATMEcell first manually collected TME cell types from multiple published studies and obtained their corresponding gene signatures. Then, a weighted cell-cell crosstalk network was constructed in the context of a specific cancer bulk tissue transcriptome data, where the weight between cells reflects both their biological function similarity and the transcriptional dysregulated activities of gene signatures shared by them. Finally, it used a network propagation algorithm to identify significantly dysregulated TME cells. Using the cancer genome atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, we illustrated that iATMEcell could identify significant abnormal cells associated with patient survival and immunotherapy response. iATMEcell was further applied to a pan-cancer analysis, which revealed that four common abnormal immune cells play important roles in the patient prognosis across multiple cancer types. Collectively, we demonstrated that iATMEcell could identify potentially abnormal TME cells based on a cell-cell crosstalk network, which provided a new insight into understanding the effect of TME cells in cancer. iATMEcell is developed as an R package, which is freely available on GitHub (https://github.com/hanjunwei-lab/iATMEcell).

A novel pathway mutation perturbation score predicts the clinical outcomes of immunotherapy.

The link between tumor genetic variations and immunotherapy benefits has been widely recognized. Recent studies suggested that the key biological pathways activated by accumulated genetic mutations may act as an effective biomarker for predicting the efficacy of immune checkpoint inhibitor (ICI) therapy. Here, we developed a novel individual Pathway Mutation Perturbation (iPMP) method that measures the pathway mutation perturbation level by combining evidence of the cumulative effect of mutated genes with the position of mutated genes in the pathways. In iPMP, somatic mutations on a single sample were first mapped to genes in a single pathway to infer the pathway mutation perturbation score (PMPscore), and then, an integrated PMPscore profile was produced, which can be used in place of the original mutation dataset to identify associations with clinical outcomes. To illustrate the effect of iPMP, we applied it to a melanoma cohort treated with ICIs and identified seven significant perturbation pathways, which jointly constructed a pathway-based signature. With the signature, patients were classified into two subgroups with significant distinctive overall survival and objective response rate to immunotherapy. Moreover, the pathway-based signature was consistently validated in two independent melanoma cohorts. We further applied iPMP to two non-small cell lung cancer cohorts and also obtained good performance. Altogether, the iPMP method could be used to identify the significant mutation perturbation pathways for constructing the pathway-based biomarker to predict the clinical outcomes of immunotherapy. The iPMP method has been implemented as a freely available R-based package (https://CRAN.R-project.org/package=PMAPscore).

CNA2Subpathway: identification of dysregulated subpathway driven by copy number alterations in cancer.

Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway).

DRviaSPCN: a software package for drug repurposing in cancer via a subpathway crosstalk network.

SUMMARY: Drug repurposing is an approach used to discover new indications for existing drugs. Recently, several computational approaches have been developed for drug repurposing in cancer. Nevertheless, no approaches have reported a systematic analysis of pathway crosstalk. Pathway crosstalk, which refers to the phenomenon of interaction or cooperation between pathways, is a critical aspect of tumor pathways that allows cancer cells to survive and acquire resistance to drug therapy. Here, we innovatively developed a system biology R-based software package, DRviaSPCN, to repurpose drugs for cancer via a subpathway (SP) crosstalk network. This package provides a novel approach to prioritize cancer candidate drugs by considering drug-induced SPs and their crosstalk effects. The operation modes mainly include construction of the SP network and calculation of the centrality scores of SPs to reflect the influence of SP crosstalk, calculation of enrichment scores of drug- and disease-induced dysfunctional SPs and weighted them by the centrality scores of SPs, evaluation of the drug-disease reverse association at the weighted SP level, identification of cancer candidate drugs and visualization of the results. Its capabilities enable DRviaSPCN to find cancer candidate drugs, which will complement the recent tools which did not consider crosstalk among pathways/SPs. DRviaSPCN may help to facilitate the development of drug discovery. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available under GPL-2 license from the CRAN website (https://CRAN.R-project.org/package=DRviaSPCN). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ZeXie decoction alleviates non-alcoholic fatty liver disease in rats: the study of genes, lipids, and gut microbiotas.

Recently, with increasing awareness of health issues, non-alcoholic fatty liver disease (NAFLD) has become an epidemic attracting global attention. As a serious chronic disease, NAFLD is clinically managed with pharmacological interventions that are usually associated with poor long-term efficacy and adverse effects. In this scenario, traditional Chinese medicine (TCM) characterized by "multiple ingredients-multiple targets-multiple pathways" shows promise as a potential option to treat NAFLD. Zexie decoction (ZXD) is a classical TCM formula that possesses favorable lipid-lowering and anti-inflammatory activities. Accumulating evidence indicates that ZXD displays robust efficacy in treating NAFLD. The effectiveness of ZXD against NAFLD has been evaluated in our previous studies. This study further examines its probable mechanism of action in an in-depth manner using multi-omic analysis based on the gut-liver axis and sheds light on the potential relationship among genes, hepatic lipid metabolites, and gut microbiotas. Totally, 71 differentially expressed genes (34 upregulated and 37 downregulated genes), 31 differential lipid molecules (8 upregulated and 23 downregulated), and 56 differential gut microbiotas (37 upregulated and 19 downregulated) were identified in the ZXD-treated group rats compared with the negative control group rats. Of these, owing to their key role in the association analysis, g_Blautia, g_Romboutsia, and g_Lactobacillus were hypothesized to be crucial gut microbiotas in the ZXD-mediated treatment of NAFLD. These microbiotas were found to synergize with key genes, such as AKR1B8, CCN1, and TNKS2, and hepatic lipid metabolites, such as glycerophospholipid and sphingomyelin, which might play a therapeutic role by regulating fatty acid synthesis, correcting lipid metabolism disorder, or reducing the inflammatory response. Overall, the present study provides fresh insights into the ZXD-mediated treatment of NAFLD, which, in turn, is expected to give a push to the modernization of TCM.

Identification of comutation in signaling pathways to predict the clinical outcomes of immunotherapy.

BACKGROUND: Immune checkpoint blockades (ICBs) have emerged as a promising treatment for cancer. Recently, tumour mutational burden (TMB) and neoantigen load (NAL) have been proposed to be potential biomarkers to predict the efficacy of ICB; however, they were limited by difficulties in defining the cut-off values and inconsistent detection platforms. Therefore, it is critical to identify more effective predictive biomarkers for screening patients who will potentially benefit from immunotherapy. In this study, we aimed to identify comutated signaling pathways to predict the clinical outcomes of immunotherapy. METHODS: Here, we comprehensively analysed the signaling pathway mutation status of 9763 samples across 33 different cancer types from The Cancer Genome Atlas (TCGA) by mapping the somatic mutations to the pathways. We then explored the comutated pathways that were associated with increased TMB and NAL by using receiver operating characteristic (ROC) curve analysis and multiple linear regressions. RESULTS: Our results revealed that comutation of the Spliceosome (Sp) pathway and Hedgehog (He) signaling pathway (defined as SpHe-comut+) could be used as a predictor of increased TMB and NAL and was associated with increased levels of immune-related signatures. In seven independent immunotherapy cohorts, we validated that SpHe-comut+ patients exhibited a longer overall survival (OS) or progression-free survival (PFS) and a higher objective response rate (ORR) than SpHe-comut- patients. Moreover, a combination of SpHe-comut status with PD-L1 expression further improved the predictive value for ICB therapy. CONCLUSION: Overall, SpHe-comut+ was demonstrated to be an effective predictor of immunotherapeutic benefit in seven independent immunotherapy cohorts and may serve as a potential and convenient biomarker for the clinical application of ICB therapy.

Hepatotoxicity of food-borne mycotoxins: molecular mechanism, anti-hepatotoxic medicines and target prediction.

Mycotoxins are metabolites produced by fungi. The widespread contamination of food and feed by mycotoxins is a global food safety problem and a serious threat to people's health. Most food-borne mycotoxins have strong hepatotoxicity. However, no effective methods have been found to prevent or treat Mycotoxin- Induced Liver Injury (MILI) in clinical and animal husbandry. In this paper, the molecular mechanisms and potential anti-MILI medicines of six food-borne MILI are reviewed, and their targets are predicted by network toxicology, which provides a theoretical basis for further study of the toxicity mechanism of MILI and the development of effective strategies to manage MILI-related health problems in the future and accelerate the development of food safety.

Deciphering the chemical constituents of Shengjiang Xiexin decoction by ultrahigh-performance liquid chromatography-quadrupole/orbitrap high-resolution mass spectrometry and the impact of 20 characteristic components on multidrug resistance-associated protein 2 in the vesicular transport assay.

Shengjiang Xiexin decoction, a traditional Chinese medical formula, has been utilized to alleviate the delayed-onset diarrhea induced by irino tecan. However, the chemical constituents of this formula and the activities of its constituents remain unclear. In this study, ultrahigh-performance liquid chromatography-quadrupole/orbitrap high-resolution mass spectrometry was employed to comprehensively analyze the chemical constituents of Shengjiang Xiexin decoction. A total of 270 components, including flavonoids, coumarins, triterpenoids, alkaloids, diarylheptanoids and others, were identified or characterized. Multidrug resistance-associated protein 2 is an efflux transporter responsible for regulating drug absorption. A total of 20 characteristic components from the formula were selected to evaluate their effects on the function of multidrug resistance-associated protein 2 using the vesicular transport assay. Glycyrrhizic acid and glycyrrhetinic acid were identified as potential multidrug resistance-associated protein 2 inhibitors, while 9 flavonoid aglycones increased the uptake of the substrate [3 H]-estradiol 17-ß-glucuronide in the vesicles. This was the first systematic investigation of the chemical constituents from Shengjiang Xiexin decoction and the effect of its characteristic components on the transporter. The results offered a basis for further exploring the detoxification mechanisms of this formula and its interactions with other drugs.

Critical Quality Control Methods for a Novel Anticoagulant Candidate LFG-Na by HPSEC-MALLS-RID and Bioactivity Assays.

A low molecular weight fucosylated glycosaminoglycan sodium (LFG-Na) is a novel anticoagulant candidate from the sea cucumber Holothuria fuscopunctata that selectively inhibits intrinsic tenase (iXase). The molecular weight, molecular weight distribution and bioactivities are the critical quality attributes of LFG-Na. The determination of these quality attributes of such an oligosaccharides mixture drug is challenging but critical for the quality control process to ensure its safety and efficacy in clinical use. Herein, the molecular weight and molecular weight distribution of LFG-Na were successfully determined using high performance size exclusion chromatography coupled with multi angle laser light scattering and refractive index detector (HPSEC-MALLS-RID). Comparing to the conventional method, HPSEC-MALLS-RID based on the refractive index increment (dn/dc) did not require the reference substances to establish the calibration curve. The acceptance criteria of LFG-Na were established, the weight-average molecular weight (Mw) should be 4000 to 6000 Da, the polydispersity (Mw/Mn) < 1.40, and the fraction with molecular weights of 1500 to 8000 Da should be no less than 80% of the total. HPSEC-MALLS-RID was also utilized for the determination of the starting material native fucosylated glycosaminoglycan (NFG) to choose a better manufacturing process. Furthermore, APTT assay was selected and the potency of anti-iXase, referring to the parallel line assay (PLA) method, was established to clarify the consistency of its biological activities. The results suggest that HPSEC-MALLS-RID and bioactivity assays are critical quality control methods for multi-component glycosaminoglycan LFG-Na. The methods also provide a feasible strategy to control the quality of other polysaccharide medicines.

Safety evaluation of Eucommia ulmoides extract.

Eucommia ulmoides Oliver is native to China and frequently used in traditional Chinese medicine formulations. However, studies show that Eucommia ulmoides extract (EUE) are potentially genotoxic and nephrotoxic. To evaluate its safety, the Ames test, bone marrow micronucleus assay and chromosomal aberration assay, along with acute (24 h) and sub-chronic (13 weeks) toxicity were conducted. EUE was non-genotoxic within the dose ranges of 0.0352-22 mg/plate (raw plant equivalent as below), 22-88 g/kg body weight and 2-20 mg/mL. The maximum tolerated dose of EUE was not less than 168 g/kg, which is 1260 times that of clinical doses in human adults. Long-term (13 weeks) administration led to dose-dependent increase in nephrotoxicity-related indices, and pathological changes in renal tissues. These changes were alleviated 5 weeks after ceasing the low dosage of 11.2 g/kg but persisted at the high dosage of 56 g/kg. Conclusively, EUE is non-genotoxic, and do not result in acute toxicity. However, long-term and high-dose administration can lead to partly reversible nephrotoxicity.

27-Hydroxycholesterol/liver X receptor/apolipoprotein E mediates zearalenone-induced intestinal immunosuppression: A key target potentially linking zearalenone and cancer.

Zearalenone (ZEN) is a mycotoxin that extensively contaminates food and feed, posing a significant threat to public health. However, the mechanisms behind ZEN-induced intestinal immunotoxicity remain unclear. In this study, Sprague-Dawley (SD) rats were exposed to ZEN at a dosage of 5 mg/kg/day b.w. for a duration of 14 days. The results demonstrated that ZEN exposure led to notable pathological alterations and immunosuppression within the intestine. Furthermore, ZEN exposure caused a significant reduction in the levels of apolipoprotein E (ApoE) and liver X receptor (LXR) (P < 0.05). Conversely, it upregulated the levels of myeloid-derived suppressor cells (MDSCs) markers (P < 0.05) and decreased the presence of 27-hydroxycholesterol (27-HC) in the intestine (P < 0.05). It was observed that ApoE or LXR agonists were able to mitigate the immunosuppressive effects induced by ZEN. Additionally, a bioinformatics analysis highlighted that the downregulation of ApoE might elevate the susceptibility to colorectal, breast, and lung cancers. These findings underscore the crucial role of the 27-HC/LXR/ApoE axis disruption in ZEN-induced MDSCs proliferation and subsequent inhibition of T lymphocyte activation within the rat intestine. Notably, ApoE may emerge as a pivotal target linking ZEN exposure to cancer development.

Zearalenone Exposure Disrupts STAT-ISG15 in Rat Colon: A Potential Linkage between Zearalenone and Inflammatory Bowel Disease.

Zearalenone (ZEN), a prevalent mycotoxin contaminating food and known for its intestinal toxicity, has been suggested as a potential risk factor for inflammatory bowel disease (IBD), although the exact relationship between ZEN exposure and IBD remains unclear. In this study, we established a rat model of colon toxicity induced by ZEN exposure to investigate the key targets of ZEN-induced colon toxicity and explore the underlying connection between ZEN exposure and IBD. Histological staining of the rat colon revealed significant pathological changes resulting from ZEN exposure (p < 0.01). Furthermore, the proteomic analysis demonstrated a notable upregulation of protein expression levels, specifically STAT2 (0.12 ± 0.0186), STAT6 (0.36 ± 0.0475) and ISG15 (0.43 ± 0.0226) in the rat colon (p < 0.05). Utilizing bioinformatics analysis, we combined ZEN exposure and IBD clinical sample databases to reveal that ZEN exposure may increase the risk of IBD through activation of the STAT-ISG15 pathway. This study identified novel targets for ZEN-induced intestinal toxicity, providing the basis for further study of ZEN exposure to IBD.

A pathway-based mutation signature to predict the clinical outcomes and response to CTLA-4 inhibitors in melanoma.

Immune checkpoint inhibitor (ICI) therapy has become a powerful clinical strategy for treating melanoma. The relationship between somatic mutations and the clinical benefits of immunotherapy has been widely recognized. However, the gene-based predictive biomarkers are less stable due to the heterogeneity of cancer at the individual gene level. Recent studies have suggested that the accumulation of gene mutations in biological pathways may activate antitumor immune responses. Herein, a novel pathway mutation signature (PMS) was constructed to predict the survival and efficacy of ICI therapy. In a dataset of melanoma patients treated with anti-CTLA-4, we mapped the mutated genes into the pathways and then identified seven significant mutation pathways associated with survival and immunotherapy response, which were used to construct the PMS model. According to the PMS model, the patients in the PMS-high group showed better overall survival (hazard ratio (HR) = 0.37; log-rank test, p < 0.0001) and progression-free survival (HR = 0.52; log-rank test, p = 0.014) than those in the PMS-low group. The PMS-high patients also showed a significantly higher objective response rate to anti-CTLA-4 therapy than the PMS-low patients (Fisher's exact test, p = 0.0055), and the predictive power of the PMS model was superior to that of TMB. Finally, the prognostic and predictive value of the PMS model was validated in two independent validation sets. Our study demonstrated that the PMS model can be considered a potential biomarker to predict the clinical outcomes and response to anti-CTLA-4 therapy in melanoma patients.

DTSEA: A network-based drug target set enrichment analysis method for drug repurposing against COVID-19.

The Coronavirus Disease 2019 (COVID-19) pandemic is still wreaking havoc worldwide. Therefore, the urgent need for efficient treatments pushes researchers and clinicians into screening effective drugs. Drug repurposing may be a promising and time-saving strategy to identify potential drugs against this disease. Here, we developed a novel computational approach, named Drug Target Set Enrichment Analysis (DTSEA), to identify potent drugs against COVID-19. DTSEA first mapped the disease-related genes into a gene functional interaction network, and then it used a network propagation algorithm to rank all genes in the network by calculating the network proximity of genes to disease-related genes. Finally, an enrichment analysis was performed on drug target sets to prioritize disease-candidate drugs. It was shown that the top three drugs predicted by DTSEA, including Ataluren, Carfilzomib, and Aripiprazole, were significantly enriched in the immune response pathways indicating the potential for use as promising COVID-19 inhibitors. In addition to these drugs, DTSEA also identified several drugs (such as Remdesivir and Olumiant), which have obtained emergency use authorization (EUA) for COVID-19. These results indicated that DTSEA could effectively identify the candidate drugs for COVID-19, which will help to accelerate the development of drugs for COVID-19. We then performed several validations to ensure the reliability and validity of DTSEA, including topological analysis, robustness analysis, and prediction consistency. Collectively, DTSEA successfully predicted candidate drugs against COVID-19 with high accuracy and reliability, thus making it a formidable tool to identify potential drugs for a specific disease and facilitate further investigation.

[Pathogenesis of glucocorticoid-induced osteoporosis based on label-free mass proteomics].

OBJECTIVE: To explore pathogenesis of glucocortocoid-induced osteoporosis(GIOP) based on label-free mass proteomics. METHODS: Twevle female Sprague-Dawley(SD) rats were randomly divided into two groups, named as sham group and GIOP group. After one-week adaptive feeding, the rats of GIOP group were administered with dexamethasone via intramuscular injection according to 2.5 mg/kg weighting, while the rats of sham group were administered with the same amount of saline, twice a week. The tibias of each group were collected after 8-week modeling and made pathological sections to confirm the success of modeling. Three samples of each group were picked up to perform label-free mass proteomics. After quality control, differentially expressed proteins were identified according to qualitative and quantitative analyses. Then gene ontology(GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, cluster analysis as well as protein-protein interaction analysis were performed using bioinformatics analysis. RESULTS: Compared with sham group, the structure of bone trabecular in GIOP group showed abnormal arrangement, uneven distribution and obvious fragmentation, which could demonstrate successful modeling. A total of 47 differentially expressed proteins (DEPs) were identified including 20 up-regulated and 27 down-regulated proteins. The expression of protein nucleophosmin 1(NPM1), adipocyte plasma membrane associated protein (APMAP), cytochromec oxidase subunit 6A1 (COX6A1) and tartrate-resistant acid phosphatase (ACP5) showed a significant difference between two groups. KEGG results showed DEPs were enriched on metabolism-related pathways, immune-related pathways and AMP-activated kinase (AMPK) signaling pathway. CONCLUSION: Protein NPM1, APMAP, COX6A1 and ACP5 showed a close relationship with pathogenesis of GIOP, which could serve as potential biomarkers of GIOP. AMPK signaling pathway played an important role in the occurrence and development of GIOP, which could be regarded as potential signaling pathway to treatment GIOP.

PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy.

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

Analysis of Tumor Microenvironment Heterogeneity among Breast Cancer Subtypes to Identify Subtype-Specific Signatures.

Breast cancer is one of the most frequent malignancies in women worldwide. According to 50-gene signature, Prediction Analysis of Microarray 50 (PAM50), breast cancer can be categorized into five molecular subtypes, and these subtypes are highly heterogeneous in different molecular characteristics. However, the landscape of their tumor microenvironment (TME) heterogeneity has not been fully researched. Using the multi-omics dataset of breast cancer from the METABRIC cohort (n = 1699), we conducted extensive analyses of TME-related features to investigate TME heterogeneity in each breast cancer subtype. We then developed a cell-based subtype set enrichment analysis to identify the subtype-specific TME cells, and further evaluate their prognostic effects. Our results illustrate that different breast cancer subtypes exhibit different TME patterns. Basal-like and HER2-enriched subtypes are associated with high immune scores, expression of most immune regulatory targets, and immune cell infiltration, suggesting that these subtypes could be defined as "immune hot" tumors and suitable for immune checkpoint blockade (ICB) therapy. In contrast, Luminal A and Luminal B subtypes are associated with low immune scores and immune cell infiltration, suggesting that these subtypes could be defined as "immune cold" tumors. Additionally, the Normal-like subtype has relatively high levels of both immune and stromal features, which indicates that the Normal-like subtype may be suitable for more diverse treatment strategies. Our study reveals the breast cancer tumor microenvironment heterogeneity across subtypes. The comprehensive analysis of breast cancer TME-related characteristics may help us to adopt a tailored treatment strategy for different subtypes of patients.

Qualitative Analysis of Drug-Containing Plasma and its Application to Quantitative Analysis and Pharmacokinetic Study of Zexie Decoction Using UPLC-MS/MS.

ZeXie Decoction (ZXD) is one of the traditional Chinese medicine formulas (TCMFs) comprising Alisma orientalis (Sam.) Juzep. (ZX) and Atractylodes macrocephala Koidz. (BZ) in 5:2 ratios and is widely employed in clinical applications since ancient times. In this study, UHPLC-QE-Orbitrap-MS was used for qualitative analysis of ZXD in rats' plasma after a single oral dose of 750 mg/kg body weight. Afterward, UHPLC-Q-TRAP-MS/MS was used for simultaneous analysis of three bioactive chemical compounds including alisol A, alisol B, and alisol A 24-acetate in ZXD's ethanol extract. Subsequently, the pharmacokinetic profiles of the three analytes were investigated in rat plasma utilizing UHPLC-Q-TRAP-MS/MS. The multiple reaction monitoring (MRM) mode for the three analytes were at m/z 508.4→383.2 for alisol A, m/z 490.4→365.2 for alisol B, and m/z 550.4→515.5 for alisol A 24-acetate. The analysis method was validated in terms of its accuracy, stability, repeatability, linearity, spiked recovery and matrix effect. As a result, twenty-five chemical constituents of ZXD were putatively identified in plasma, and rapid, sensitive, and accurate methods were established for the quantitative analysis and pharmacokinetic study of ZXD. The findings of this study can provide a scientific base for further study of in vivo pharmacokinetics of TCMFs.

Zearalenone-14-Glucoside Is Hydrolyzed to Zearalenone by ß-Glucosidase in Extracellular Matrix to Exert Intracellular Toxicity in KGN Cells.

As one of the most important conjugated mycotoxins, zearalenone-14-glucoside (Z14G) has received widespread attention from researchers. Although the metabolism of Z14G in animals has been extensively studied, the intracellular toxicity and metabolic process of Z14G are not fully elucidated. In this study, the cytotoxicity of Z14G to human ovarian granulosa cells (KGN) and the metabolism of Z14G in KGN cells were determined. Furthermore, the experiments of co-administration of ß-glucosidase and pre-administered ß-glucosidase inhibitor (Conduritol B epoxide, CBE) were used to clarify the mechanism of Z14G toxicity release. Finally, the human colon adenocarcinoma cell (Caco-2) metabolism model was used to verify the toxicity release mechanism of Z14G. The results showed that the IC50 of Z14G for KGN cells was 420 µM, and the relative hydrolysis rate of Z14G on ZEN was 35% (25% extracellular and 10% intracellular in KGN cells). The results indicated that Z14G cannot enter cells, and Z14G is only hydrolyzed extracellularly to its prototype zearalenone (ZEN) by ß-glucosidase which can exert toxic effects in cells. In conclusion, this study demonstrated the cytotoxicity of Z14G and clarified the toxicity release mechanism of Z14G. Different from previous findings, our results showed that Z14G cannot enter cells but exerts cytotoxicity through deglycosylation. This study promotes the formulation of a risk assessment and legislation limit for ZEN and its metabolites.