KEGG: kyoto encyclopedia of genes and genomes

Differential expression and functional analysis of circular RNAs and m6A modifications in children with Philadelphia chromosome-positive acute lymphoblastic leukemia

Philadelphia chromosome-positive (Ph+) acute lymphoblastic leukaemia (ALL) in childhood is associated with dismal outcomes, in large part due to challenges in diagnosis and monitoring therapeutic efficacy. Recent studies suggest that circular RNAs (circRNAs) are potential diagnostic and prognostic biomarkers for various tumours. to indicate the potential role of circRNAs in identifying or serving as novel targets for treatments. Here, we analysed CircRNA expression profiles in samples from three Ph+ ALL patients at diagnosis (CK1 group), on day 19 after treatment (T1 group) and in first complete remission (day 46 after treatment, T2 group), as well as one Ph- ALL patient at diagnosis (CK2 group). A total of 922 differentially expressed circRNAs (DECs) potentially associated with RNA degradation, microRNAs in cancer, propanoate metabolism and ubiquitin-mediated proteolysis were found (626 upregulated and 296 downregulated) between the CK1 and CK2 groups. In addition, we identified 224 DECs (129 upregulated and 95 downregulated) between the CK1 and T1 groups and 225 DECs (136 upregulated and 89 downregulated) between the CK1 and T2 groups, including 136 for which their expression was upregulated and 89 for which their expression was downregulated. The levels of hsa_circ_0012152 and hsa_circ_0009024 were significantly increased in Ph+ ALL patients, the changes in the levels of these circRNAs were confirmed by qRT‒PCR, indicating their potential as diagnostic biomarkers. Most upregulated DECs underwent N6-methyladenosine (m6A) modification noting the specific roles that are now better understood based on the circRNAs and DECs identified, and ideally suggesting how the findings could impact the diagnosis and treatment of Ph+ ALL The findings of this study increase our understanding of the roles of m6A-modified circRNAs in the pathogenesis of Ph+ ALL.

Identification and verification of mitochondria-related genes biomarkers associated with immune infiltration for COPD using WGCNA and machine learning algorithms

Mitochondrial dysfunction plays a pivotal role in the pathogenesis of chronic obstructive pulmonary disease (COPD). This study combines bioinformatics analysis with machine learning to elucidate potential key mitochondrial-related genes associated with COPD and its immune microenvironment. We utilized the limma package and Weighted Gene Co-expression Network Analysis (WGCNA) to analyze datasets from the Gene Expression Omnibus (GEO) database (GSE57148), identifying 12 key differentially expressed mitochondrial genes (MitoDEGs). Using 12 distinct machine learning algorithms (comprising 143 predictive models), we identified the optimal diagnostic model, which includes five pivotal MitoDEGs: ERN1, FASTK, HIGD1B, NDUFA7 and NDUFB7. The diagnostic specificity and sensitivity of each gene, as well as the diagnostic model itself, were evaluated using Receiver operating characteristic (ROC) curves. This model demonstrated high specificity in the validation cohorts (GSE76925, GSE151052, GSE239897). Expression analysis revealed upregulation of ERN1 and downregulation of FASTK, HIGD1B, NDUFA7 and NDUFB7 in COPD patients. Spearman's correlation analysis indicated a significant association between MitoDEGs and immune cell infiltration, with ERN1 expression positively correlated with neutrophil infiltration and the other genes negatively correlated. The GABA receptor modulator androstenol was identified as a potential therapeutic candidate. In vivo studies confirmed reduced mRNA expression of HIGD1B and NDUFB7 in COPD mice. These findings elucidate mitochondrial-immune interactions in COPD and highlight novel diagnostic and therapeutic targets.

A signature based on efferocytosis-related genes for the evaluation of prognosis and the tumour microenvironment in gastric cancer

Gastric cancer (GC) is a highly malignant tumor of the digestive system. The process of efferocytosis has been confirmed to be closely associated with tumor progression and microenvironment remodeling. Nevertheless, the mechanism of efferocytosis in GC remains unclear. This study integrates single-cell RNA sequencing (scRNA-seq) datasets with the TCGA transcriptome data for GC, focusing on the expression and distribution of efferocytosis-related genes (ERGs) at the single-cell level in GC. The prognostic features of ERGs are determined by Cox and LASSO analysis. And we analyzed and evaluated the differences between the two groups of patients in terms of long-term prognosis, immune infiltration, expression of immune checkpoints, and response to chemotherapeutic drugs. Seven cell types were identified from 10 GC samples. ERGs were mainly concentrated in macrophages, dividing macrophages into 5 cell subtypes. LASSO combined with Cox ultimately confirmed 4 independent prognostic genes, and a prognostic nomogram was constructed based on gene risk scores and clinical features, which was validated in an independent dataset. Further studies revealed that ERGs were closely related to the patient's immune cell infiltration (especially M2 macrophages), immunotherapy response, and drug sensitivity. We developed an ERG-based predictive model that could serve as a valuable tool for prognosis assessment and decision support in the context of immunotherapy and chemotherapy.

Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis

Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however, its molecular mechanism remains unclear. Herein, hub genes and regulatory mechanisms in sepsis was investigated. The GSE65682 and GSE95233 datasets were extracted from public databases. Differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted in GSE65682 to identify differentially expressed genes (DEGs) and key module genes. Candidate genes were derived by intersecting with SUMOylation-related genes (SUMO-RGs). The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were utilized to identify significant feature genes. The convergence of those genes was utilized for diagnostic assessment and expression validation. Hub genes were defined as those exhibiting an area under the curve (AUC) greater than 0.7, significant gene expression, and a consistent trend. Localization and functional analyses of hub genes were conducted to enhance the understanding of these genes. Immune analysis, regulatory network construction, and drug prediction were performed. Six hub genes were identified: RORA, L3MBTL2, PHC1, RPA1, CHD3, and RANGAP1. These genes possessed considerable diagnostic significance for sepsis and were also markedly downregulated in the condition. Hub genes were predominantly enriched in the ribosome pathway and exhibited a strong correlation with differential immune cells. Activated CD8 + T cells exhibited a positive correlation with RORA. Based on the predicted and established regulatory network, AC004687.1 was observed to modulate PHC1 expression via hsa-miR- 142 - 5p. A total of six hub genes (RORA, L3MBTL2, PHC1, RPA1, CHD3, and RANGAP1) associated with SUMOylation was identified in sepsis in the current study. The findings are likely to aid in the differentiation between control and disease states, offering substantiation for the diagnosis of sepsis.

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction

Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data from glioblastoma (GBM) and low-grade glioma (LGG) samples, we identified 55 distinct cell states via the EcoTyper framework, validated for stability and prognostic impact in an independent cohort. We constructed multi-omics datasets of 620 samples, integrating transcriptomic, copy number variation (CNV), somatic mutation (MUT), Microbe (MIC), EcoTyper result data. A scRNA-seq enhanced Self-Normalizing Network-based glioma prognosis model achieved a C-index of 0.822 (training) and 0.817 (test), with AUC values of 0.867, 0.876, and 0.844 at 1, 3, and 5 years in the training set, and 0.820, 0.947, and 0.936 in the test set. Gradient attribution analysis enhanced the interpretability of the model and identified key molecular markers. The classification into high- and low-risk groups was validated as an independent prognostic factor. HDAC inhibitors are proposed as potential treatments. This study demonstrates the potential of integrating scRNA-seq and multi-omics data for robust glioma prognosis and clinical decision-making support.

Comparative microRNAs profile of Schistosoma japonicum male worms derived from single-sex and bisexual infections: Implications of the multifunctional role of microRNA

Schistosoma japonicum is a dioecious parasite that requires constant pairing between male and female worms for female maturation and egg production. MicroRNAs (miRNAs) play crucial roles in regulating various biological processes, including parasite development and host-pathogen interactions, but their functions in schistosomes remain largely unexplored. This study aimed to investigate the differentially expressed miRNAs (DEMs) between mated male (MM) and single-sex male (SM) worms to gain insights into their regulatory roles in schistosome reproduction. Total RNA was extracted from 28-day-old MM and SM worms, followed by small RNA sequencing to identify DEMs. Bioinformatics analyses were used to predict the biological functions of DEM target genes. Comparative analysis with previously published miRNA datasets helped identify potentially significant miRNAs. Quantitative PCR (qPCR) validated the expression of selected miRNAs and mRNA levels of some target genes. A total of 20 DEMs were identified, with 9 upregulated in MM worms and 11 in SM worms. These DEMs may regulate processes such as intracellular transport, RNA processing, and cellular homeostasis. The study provides novel insights into the biological differences between SM and MM worms, suggesting that these miRNAs could be key regulators of parasite development and host adaptation, with potential diagnostic and therapeutic applications in schistosomiasis.

Collaborative metabolic curation of an emerging model marine bacterium, Alteromonas macleodii ATCC 27126

Inferring the metabolic capabilities of an organism from its genome is a challenging process, relying on computationally-derived or manually curated metabolic networks. Manual curation can correct mistakes in the draft network and add missing reactions based on the literature, but requires significant expertise and is often the bottleneck for high-quality metabolic reconstructions. Here, we present a synopsis of a community curation workshop for the model marine bacterium Alteromonas macleodii ATCC 27126 and its genome database in BioCyc, focusing on pathways for utilizing organic carbon and nitrogen sources. Due to the scarcity of biochemical information or gene knock-outs, the curation process relied primarily on published growth phenotypes and bioinformatic analyses, including comparisons with related Alteromonas strains. We report full pathways for the utilization of the algal polysaccharides alginate and pectin in contrast to inconclusive evidence for one-carbon metabolism and mixed acid fermentation, in accordance with the lack of growth on methanol and formate. Pathways for amino acid degradation are ubiquitous across Alteromonas macleodii strains, yet enzymes in the pathways for the degradation of threonine, tryptophan and tyrosine were not identified. Nucleotide degradation pathways are also partial in ATCC 27126. We postulate that demonstrated growth on nitrate as sole nitrogen source proceeds via a nitrate reductase pathway that is a hybrid of known pathways. Our evidence highlights the value of joint and interactive curation efforts, but also shows major knowledge gaps regarding Alteromonas metabolism. The manually-curated metabolic reconstruction is available as a "Tier-2" database on BioCyc.

E2F expression profiling-based subtypes in head and neck squamous cell carcinoma: clinical relevance, prognostic implications, and personalized therapy

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous malignancy with poor prognosis. Dysregulation of E2F transcription factors (E2Fs), which control cell proliferation and apoptosis, is implicated in HNSCC pathogenesis. This study explores HNSCC molecular heterogeneity via E2Fs expression, identifies distinct subtypes, and develops a prognostic model that integrates gene expression, immune infiltration, and drug sensitivity.

METHODS

We analyzed the TCGA-HNSC dataset (n = 494) and classified samples based on the expression of eight E2Fs using ConsensusClusterPlus. The optimal number of clusters (k = 2) was determined with the getOptK() function, which assesses cluster stability via internal consistency metrics. Differentially expressed genes between subtypes were identified with limma, and functional annotation was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. A prognostic model was constructed using LASSO regression on genes significant in univariate Cox analysis and validated in an independent GSE41613 dataset (n = 97). Immune cell infiltration was estimated using CIBERSORT, and drug sensitivity predicted via pRRophetic. Confounding factors such as HPV and smoking status were not included due to incomplete data.

RESULTS

Two distinct E2F-based subtypes emerged. Cluster 1, characterized by lower E2Fs expression, exhibited poorer overall survival (log-rank, p = 0.035) and was enriched in genes related to epidermal development, keratinocyte differentiation, and IL-17 signaling. In contrast, Cluster 2 showed higher E2Fs expression, better survival, and enrichment in genes associated with DNA replication and repair. Notably, high-risk patients demonstrated increased infiltration of M0 and M2 macrophages (p < 0.05), suggesting an immunosuppressive tumor microenvironment that adversely affects prognosis. Our seven-gene prognostic model (AREG, CXCL14, FAM83E, FDCSP, ARHGAP4, EPHX3, and SPINK6) exhibited robust performance with AUCs of 0.692, 0.673, and 0.679 for 1-, 3-, and 5-year survival, a C-index of 0.66, and good calibration. High-risk patients also showed greater sensitivity to targeted agents such as pazopanib and imatinib.

CONCLUSIONS

These findings reveal two distinct E2F-based molecular subtypes of HNSCC that differ in prognosis, functional pathways, immune infiltration, and drug sensitivity. The prognostic model offers valuable risk stratification and identifies potential biomarkers and therapeutic targets, warranting further experimental and clinical validation.

EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals

The known universe of uncharacterized proteins is expanding far faster than our ability to annotate their functions through laboratory study. Computational annotation approaches rely on similarity to previously studied proteins, thereby ignoring unstudied proteins. Coevolutionary approaches hold promise for injecting new information into our knowledge of the protein universe by linking proteins through 'guilt-by-association'. However, existing coevolutionary algorithms have insufficient accuracy and scalability to connect the entire universe of proteins. We present EvoWeaver, a method that weaves together 12 signals of coevolution to quantify the degree of shared evolution between genes. EvoWeaver accurately identifies proteins involved in protein complexes or separate steps of a biochemical pathway. We show the merits of EvoWeaver by partly reconstructing known biochemical pathways without any prior knowledge other than that available from genomic sequences. Applying EvoWeaver to 1545 gene groups from 8564 genomes reveals missing connections in popular databases and potentially undiscovered links between proteins.

BCAT2 binding to PCBP1 regulates the PI3K/AKT signaling pathway to inhibit autophagy-related apoptosis and ferroptosis in prostate cancer

Prostate cancer (PCa) has emerged as a predominant cause of cancer-related mortality among men globally. The mechanisms of branched-chain amino acids (BCAAs) contributing to the development of PCa remain inadequately elucidated. The objective of this study was to examine the involvement of BCAAs and BCAT2 in tumorigenesis. BCAAs exhibited elevated expression levels in PCa tissues and cells. Among the critical enzymes involved in the BCAA metabolic pathway, only BCAT2 demonstrated significant expression in PCa and was closely associated with tumor progression and patient prognosis. RNA sequencing along with related functional experiments indicated that BCAT2 can inhibit autophagy, autophagy-related apoptosis, and ferroptosis in PCa. Furthermore, the results of co-immunoprecipitation, mass spectrometry, and other methodologies established that PCBP1, as a downstream protein interacting with BCAT2, co-regulates the PI3K/AKT pathway, thereby influencing progression of PCa. Moreover, BCAT2 interacted with PCBP1 at Leucine 239 to collaboratively regulate the PI3K/AKT signaling pathway, which is crucial for the initiation and progression of PCa. Targeting BCAT2 may represent a promising therapeutic strategy to prevent proliferation of PCa.

Quantitative proteomics analysis of triple-negative breast cancers

Triple-negative breast cancer (TNBC) accounts for approximately 15% of all Breast Cancer (BC) cases with poorer prognosis and clinical outcomes compared to other BC subtypes due to greater tumor heterogeneity and few therapeutically targetable oncogenic drivers. To reveal actionable pathways for anti-cancer treatment, we use a proteomic approach to quantitatively compare the abundances of 6306 proteins across 55 formalin-fixed and paraffin-embedded (FFPE) TNBC tumors. We identified four major TNBC clusters by unsupervised clustering analysis of protein abundances. Analyses of clinicopathological characteristics revealed associations between the proteomic profiles and clinical phenotypes exhibited by each subtype. We validate the findings by inferring immune and stromal cell type composition from genome-wide DNA methylation profiles. Finally, quantitative proteomics on TNBC cell lines was conducted to identify in vitro models for each subtype. Collectively, our data provide subtype-specific insights into molecular drivers, clinicopathological phenotypes, tumor microenvironment (TME) compositions, and potential pharmacologic vulnerabilities for further investigations.

Identification of pivotal genes and regulatory networks associated with SAH based on multi-omics analysis and machine learning

Subarachnoid hemorrhage (SAH) is a disease with high mortality and morbidity, and its pathophysiology is complex but poorly understood. To investigate the potential therapeutic targets post-SAH, the SAH-related feature genes were screened by the combined analysis of transcriptomics and metabolomics of rat cortical tissues following SAH and proteomics of cerebrospinal fluid from SAH patients, as well as WGCNA and machine learning. The competitive endogenous RNAs (ceRNAs) and transcription factors (TFs) regulatory networks of the feature genes were constructed and further validated by molecular biology experiments. A total of 1336 differentially expressed proteins were identified, including 729 proteins downregulated and 607 proteins upregulated. The immune microenvironment changed after SAH and the changement persisted at SAH 7d. Through multi-omics and bioinformatics techniques, five SAH-related feature genes (A2M, GFAP, GLIPR2, GPNMB, and LCN2) were identified, closely related to the immune microenvironment. In addition, ceRNAs and TFs regulatory networks of the feature genes were constructed. The increased expression levels of A2M and GLIPR2 following SAH were verified, and co-localization of A2M with intravascular microthrombus was demonstrated. Multiomics and bioinformatics tools were used to predict the SAH associated feature genes confirmed further through the ceRNAs and TFs regulatory network development. These molecules might play a key role in SAH and may serve as potential biological markers and provide clues for exploring therapeutic options.

Time- and dose-dependent effects of CIGB-300 on the proteome of lung squamous cell carcinoma

Proteome-wide scale in a dose - and time-depending setting is crucial to fully understand the pharmacological mechanism of anticancer drugs as well as identification of candidates for drug response biomarkers. Here, we investigated the effect of the CIGB-300 anticancer peptide at IC50 and IC80 doses during 1 and 4 h of treatment on the squamous lung cancer cell (NCI-H226) proteome. An overwhelming dose-dependent inhibitory effect with minor up-regulated proteins was observed by increasing CIGB-300 dose level. Functional enrichment was also CIGB-300 dose-dependent with common or exclusively regulated proteins in each dose and time settings. A protein core involving small molecule biosynthesis, aldehyde metabolism and metabolism of nucleobases was regulated irrespectively to the dose or the treatment time. Importantly, a group of proteins linked to NSCLC tumor biology, poor clinical outcome and some Protein Kinase CK2 substrates, were significantly regulated by treating with both CIGB-300 doses. Likewise, we observed a consistent downregulation of different proteins that had been already reported to be inhibited by CIGB-300 in lung adenocarcinoma and acute myeloid leukemia. Overall, our proteomics-guided strategy based on time and drug dose served to uncover novel clues supporting the CIGB-300 cytotoxic effect and also to identify putative pharmacodynamic biomarkers in NSCLC.

Variant-specific disruption to notch signalling in PAX6 microphthalmia and aniridia patient-derived hiPSC optic cup-like organoids

The homeobox-containing transcription factor PAX6 is a key regulator of eye development. Pathogenic heterozygous PAX6 variants lead to variable ocular phenotypes, most commonly haploinsufficiency-induced aniridia. Missense variants are typically associated with milder ocular conditions, although variants in the DNA-binding paired domain which alter target binding lead to severe ocular phenotypes including bilateral microphthalmia, similar to SOX2-anophthalmia syndrome. However, the variant-specific pathway disruption resulting in phenotypic heterogeneity is not well understood. To investigate pathogenic mechanisms of PAX6 variants, transcriptomic and chromatin accessibility analysis was performed on hiPSC derived 3D optic cup-like organoids generated from patients with variants (i) PAX6N124K displaying combined microphthalmia, aniridia and optic nerve coloboma, and (ii) PAX6R261X exhibiting typical aniridia. Total RNA sequencing analysis revealed downregulation of SOX2 in missense PAX6N124K cups compared to both wildtype and PAX6R261X haploinsufficient aniridia controls, along with Notch signalling components and markers of proliferation and differentiation. Transcription factor binding motifs of Notch-related genes were also found to be differentially bound in PAX6N124K cups through ATACseq footprinting analysis. Our analysis of PAX6-related oculopathies using in vitro models reveals disruption to DNA binding perturbs SOX2 and Notch signalling, contributing to severe ocular phenotypes in patients with missense changes in the paired domain. This work reveals a previously unestablished role for PAX6 in SOX2 and Notch signalling regulation during early oculogenesis, as well as illuminating disease mechanisms underlying variant-specific ocular phenotypes and genotype-phenotype correlations. These novel insights can influence clinical care, and provide valuable data on potential therapeutic targets, which can guide future translational research.

The potential for coupled organic and inorganic sulfur cycles across the terrestrial deep subsurface biosphere

Organosulfur compounds (OrgS) are fundamental components of life's biomass, yet the cycling of these compounds in the terrestrial deep subsurface, one of Earth's largest ecosystems, has gone relatively unexplored. Here, we show that all subsurface microbial genomes reconstructed from Soudan Underground Mine State Park have the capacity to cycle organic sulfur species. Our findings suggest that OrgS degradation may be an integral link between the organic and inorganic sulfur cycle via the production of sulfite and sulfide. Furthermore, despite isolation from surface ecosystems, most Soudan microorganisms retained genes for dimethylsulfoniopropionate and taurine biosynthesis. Metagenomic analyses of an additional 54 deep subsurface sites spanning diverse lithologies revealed the capacity for OrgS cycling to be widespread, occurring in 89% of assembled metagenomes. Our results indicate that consideration of OrgS cycling may be necessary to accurately constrain sulfur fluxes, discern the energetic limits of deep life, and determine the impact of deep subsurface biogeochemical sulfur cycling on greater Earth system processes.

Screening and metabolic analysis of high-efficiency molluscicidal bacteria based on atmospheric and room temperature plasma mutagenesis

Oncomelania hupensis is the only intermediate host of Schistosoma japonicum, highlighting the need for developing low-toxicity, efficient, and economical molluscicides to control schistosomiasis transmission and prevalence. This study screened for effective molluscicides using O. hupensis in an immersion-based biological assay. Bsp dustable powder (DP) emerged as the most effective molluscicide among seven microbial pesticide samples tested. The dominant strain, designated 3-4, was isolated and identified as Bacillus subtilis using 16S rDNA gene sequencing. Following atmospheric and room temperature plasma (ARTP) mutagenesis, a mutant strain library containing 214 strains of bacteria was obtained. Most of the 139 mutant strains showed no significant difference compared with parental strain and 60 strains showed a decrease. Meanwhile, 15 mutant strains with higher molluscicidal effects were obtained. ARTP-129 and ARTP-154, exhibiting the highest positive mutation rates, demonstrated a 64.51 % increase in mortality compared to the parental strain 3-4. Metabolomic analysis revealed that the mutant strains may alter molluscicidal toxicity by regulating the synthesis pathways of metabolites such as l-pyroglutamic acid (PGA). These findings suggest the potential of ARTP mutagenesis for developing novel and effective molluscicides for schistosomiasis control.

Mechanisms of chemotherapy failure in refractory/relapsed acute myeloid leukemia: the role of cytarabine resistance and mitochondrial metabolism

Acute myeloid leukemia (AML) is an aggressive hematological malignancy. Patients with wild-type FLT3 relapsed or refractory (R/R) AML face significant therapeutic challenges due to the persistent lack of effective treatments. A comprehensive understanding of the mechanisms underlying chemotherapy resistance is needed to the development of effective treatment strategies. Therefore, we investigated the molecular mechanisms underlying cytarabine (Ara-C) resistance and daunorubicin (DNR) tolerance in Ara-C-resistant RHI-1 cells derived from the wild-type FLT3 AML cell line SHI-1. Quantitative analysis of intracellular drug concentrations, proteomics, and phosphoproteomics showed that DNR resistance in Ara-C-resistant RHI-1 cells is driven by metabolic remodeling toward mitochondrial metabolism, upregulation of DNA repair pathways, and enhanced reactive oxygen species (ROS) detoxification rather than reduced drug uptake. Moreover, targeting these compensatory mechanisms, particularly the OXPHOS complex I proteins, significantly improved the efficacy of both Ara-C and DNR. Conclusively, these findings highlight mitochondrial metabolism and DNA repair as critical factors in chemotherapy resistance and offer valuable insights into potential therapeutic targets for enhancing treatment outcomes in patients with wild-type FLT3 R/R AML.

Probiotics promote cellular wound healing responses by modulating the PI3K and TGF-β/Smad signaling pathways

BACKGROUND

Skin wound healing represents a dynamic and intricate biological process involving the coordinated efforts of various cellular and molecular components to restore tissue integrity and functionality. Among the myriads of cellular events orchestrating wound closure, fibroblast migration and the regulation of fibrosis play pivotal roles in determining the outcome of wound healing. In recent years, probiotic therapy has emerged as a promising strategy for modulating wound healing and fibrosis. Here, we aim to investigate the effect of bacterial probiotics on cell migration and anti-fibrotic response of human dermal fibroblast (HDFs).

METHODS

Probiotic mixture BioK was co-cultured with HDFs in vitro to assess its impact on fibroblast migration, gene expression, and protein production associated with important processes in wound healing. Gene expression was investigated by transcriptomic analysis and confirmed by RT-qPCR. Protein levels of the identified genes were evaluated by ELISA. The role of lactic acid, produced by BioK, in mediating pH-related effects on fibroblast activity was further examined.

RESULTS

We observed that BioK effectively promoted HDFs migration in vitro, which was found to be related to the up-regulation of genes involved in the phosphoinositide 3-kinase (PI3K) signaling pathways such as Paxillin, PI3K, PKC and ITG-β1. Interestingly, we also found that BioK down-regulated the expression of Nox-4, α-SMA and Col-I in TGF-Smad signaling pathways, which are involved in the differentiation of fibroblasts to myofibroblasts, and extracellular matrix type I collagen production, demonstrating its potential in reducing formation of fibrosis and scars. One of the acting factors for such down-regulation was identified to be BioK-produced lactic acid, which is known to lower the surrounding pH and to play a major role in fibroblast activity and wound healing.

CONCLUSIONS

This study demonstrates BioK's beneficial effects on fibroblast migration and its potential to mitigate fibrosis through pH modulation and pathway-specific gene regulation. These findings enhance our understanding of probiotic action on wound healing and offer promising therapeutic insights for the reduction of scar formation.

CLINICAL TRIAL NUMBER

Not applicable.

CDC6 as early biomarker for myocardial infarction with acute cellular senescence and repair mechanisms

We aimed to the exploration of genes related to the cell cycle and the mechanisms of cardiac cell repair and senescence post-MI. The sequencing dataset of myocardial infarction in mice (GSE161427) was downloaded from the Gene Expression Omnibus (GEO) database, yielding 894 upregulated differentially expressed genes (DEGsup). Additionally, 494 senescence-related genes (SRGs) were obtained from the CellAge database. The overlapping differentially expressed genes (DEupSGs) between these two sets were identified using the R software. WGCNA using the GSE59867 dataset revealed a highly related module to MI. The intersection of core genes from the purple module and DEupSGs yielded 12 genes. Through machine learning and PPI analysis, two target genes related to MI and cellular senescence were identified: CDC6 and PLK1. ROC curve analysis using the MI animal myocardial sample dataset GSE775 and the patient blood sample dataset GSE60993 indicated that CDC6 expression has high diagnostic value for MI and cellular senescence, with differential expression levels at various times post-infarction.These results show that CDC6 is specifically upregulated in the early stages of MI, and both in vivo and ex vivo model results are consistent with bioinformatics findings. Additionally, overexpression of CDC6 in the early oxygen-glucose deprivation (OGD) model in vitro increased the expression of genes mediating cardiac repair. Interestingly, when ABT263 was used to clear senescent cells, the expression of genes mediating repair in primary cardiac cells decreased, suggesting that acute ischemic hypoxia early on, CDC6-mediated acute cardiac cell senescence may promote early cardiac repair. This finding may provide new insights into the monitoring and assessment of cardiac cell senescence and repair in the early stages of MI.

Mechanism Exploration of Dietary Supplement Astaxanthin on Improving Atherosclerosis through an Integrated Strategy Encompassing Artificial Intelligence Virtual Screening and Experimental Validation

Atherosclerosis (AS) is a major and common pathological basis of ischemic intestinal infarction, myocardial infarction, stroke, renal failure, and other highly lethal and disabling diseases. Current pharmacological interventions (e.g., statins) often cause adverse effects, limiting their long-term use. Natural compounds, with their multitarget efficacy and superior safety profiles, have emerged as promising alternatives for AS treatment. As a potent antioxidant carotenoid, astaxanthin exhibits unique therapeutic potential by simultaneously targeting inflammation, oxidative stress, and lipid metabolism, which are key drivers of AS pathogenesis. This study will systematically decipher astaxanthin's therapeutic mechanisms through an integrative strategy encompassing artificial intelligence virtual screening and experimental validation. Notably, five proteins, including CTSD, DPP4, FABP5, ITGAL, and MMP9, were identified as core targets for astaxanthin intervention in AS via network pharmacology and machine learning. Meanwhile, the results from molecular dynamic simulations confirmed that these core targets can stable binding with astaxanthin. Furthermore, in vitro experiments further validated astaxanthin can inhibit foam cell formation, restore redox balance, and suppress inflammation. Moreover, a close correlation has been found between them. These findings position astaxanthin as a multitarget natural agent to combat AS, addressing both efficacy advantage and safety concerns of current therapies.

Nontargeted Urinary Profiling Strategy for Endocrine-Disrupting Chemicals in Women with Ovarian Malignancies

Endocrine-disrupting chemicals (EDCs), including known and unknown parent compounds, their metabolites, and transformation products, are pervasive in daily life, posing increasing risks to human health and the environment. This study employed a high-resolution mass spectrometry-based nontargeted screening approach, integrating polar (HILIC) and reversed-phase separations to expand the chemical space coverage and, supported by open-science tools and resources, evaluated urinary chemical profiles to assess internal EDC exposure. Among 106 annotated biomarkers of exposure, six exhibited significantly higher normalized intensities in patients with ovarian malignancies compared to healthy controls (p < 0.05). This suggests their greater exposure to phthalates (diethylhexyl phthalate and diethyl phthalate), pesticides (metolachlor metabolite and 4-nitrophenol), a UV filter (benzophenone-1), and an industrial byproduct (4-methyl-2-nitrophenol). These compounds may interfere with hormonal regulation, potentially contributing to cancer development. While these findings highlight potential differences in internal EDC exposure, the study primarily demonstrates the applicability of nontargeted urinary profiling for chemical exposure assessment. By providing new insights into EDCs burden and its pathological implications, this work contributes to advancing next-generation chemical risk assessment within the European Partnership for the Assessment of Risks from Chemicals initiative and supports the development of preventive strategies to mitigate environmental cancer risks.

Safety profile of sikamat virus and its oncolytic potential in leukemic cells and cancer stem cells

Leukaemia remains a global health concern. The oncotherapy resistance of leukaemia might be due to the existence of cancer stem cell populations. This study investigated the therapeutic potential of Sikamat virus (PRV7S), a Pteropine orthoreovirus, as an oncolytic virus against acute myeloid leukaemia (AML) and chronic myeloid leukaemia (CML). Using AML and CML cell lines (THP-1 and K562), as well as an AML-M5-derived cancer stem cell (CSC) model, PRV7S was shown to infect these leukaemic cells, replicate within them, and reduce their viability. PRV7S-induced cell death was associated with caspase-mediated apoptosis without significant cell cycle arrest. Transcriptomic and proteomic analyses revealed that PRV7S infection altered several cell death pathways, including apoptosis and necroptosis, highlighting its complex cell death mechanisms. PRV7S replicated efficiently in infected cells, though it did not cause persistent infection. An in vivo safety evaluation in immunocompetent mice demonstrated that PRV7S was well-tolerated, showing no adverse effects on survival, body weight, or histopathology, and no evidence of viral persistence. These findings suggest PRV7S as a promising oncolytic candidate for myeloid leukaemia, with potential efficacy against CSCs and a favourable safety profile. In conclusion, the study provides new insights into the cellular pathways involved in PRV7S-mediated oncolysis and supports further exploration of PRV7S's potential against resistant leukaemic and solid tumours.

To establish and validate autophagy related biomarkers for the diagnosis of IgA nephropathy

IgA nephropathy (IgAN) is one of the most common immune-related primary glomerular diseases. The pathological mechanism of this disease is complex, and the specific pathogenesis is still unclear. To obtain a comprehensive understanding of its molecular mechanism and to provide new perspectives regarding the detection and treatment of the disease, this study investigated the role of immune cells in IgAN, as well as the role of autophagy-related biomarkers in IgAN development. The original datasets GSE93798, GSE35487, GSE58539, GSE116626 and GSE115857 were downloaded from Gene Expression Omnibus (GEO) and were further integrated and analyzed. The differentially expressed genes (DEGs) between IgAN and healthy control (HC) group were identified by the "limma" R package. The gene ontology (GO) function, Kyoto Encyclopedia of Genes and Genome (KEGG) pathway, GeneSet Enrichment Analysis (GSEA) and DisGeNet enrichment were adopted to analyze the genes from the intersection of DEGs. The hub genes were screened by the square least absolute shrinkage and selection operator (LASSO) and cross validation. Immune cell infiltration was analyzed using CIBERSORT. The correlation between hub genes and infiltrating immune cells was calculated by R software. For the purpose of exploring the value of hub genes for diagnosing IgAN, a receiver operating characteristic (ROC) curve was constructed. Finally, Real-time quantitative polymerase chain reaction (qRT-PCR) was used to verify the relative mRNA level of the AT-DEGs. 12 DEGs were screened out. Enrichment analysis revealed that autophagy-related DEGs (AT-DEGs) were mainly related to intrinsic apoptotic signaling pathway, cellular response to external stimulus, transcription repressor complex and other cellular functions, KEGG pathways enriched by AT-DEGs mainly included biological metabolic pathways related to autophagy, while DisGeNET analysis showed that these AT-DEGs were mainly related to immunological diseases. The optimal six hub genes were obtained by lasso analysis as potential biomarkers for IgAN. ROC curve analysis showed that 4 of the 6 HUB genes had great diagnostic value. Immune infiltration results showed B cells memory, macrophages M2, NK cells activated, T cells CD4+ memory resting, and monocytes are the predominant immune cells with the development of IgAN. The qRT-PCR results showed that, compared to the NC group, SIRT1 mRNA expression in PBMCs from IgAN patients was significantly reduced, while BAG3, CDKN1A, and FOS mRNA levels were markedly elevated. SIRT1, BAG3, COKN1A and FOS can be considered as effective biomarkers related to autophagy for the diagnosis of IgAN. These findings suggest some potential new serum biomarkers for IgAN diagnosis.

WGCNA analysis reveals hub genes in the Hemarthria compressa roots in response to waterlogging stress

Hemarthria compressa is a high-quality forage resource in China. In recent years, waterlogging has frequently occurred, adversely affecting the growth and development of H. compressa. In order to investigate the physiological and molecular response mechanisms of H. compressa under waterlogging stress and identify hub genes involved in waterlogging tolerance, H. compressa roots from the GY (waterlogging-tolerant) and N1291 (waterlogging-sensitive) cultivars were selected as experimental materials in this study. The physiological indexes of H. compressa were measured, and transcriptome sequencing was carried out after 8 h and 24 h of waterlogging stress, with 0 h used as the control group. Superoxide dismutase (SOD) and peroxidase (POD) activities were significantly increased in both GY and N1291 under waterlogging stress (P < 0.05). Weighted gene co-expression network analysis (WGCNA) identified a total of four modules significantly associated with waterlogging stress (r>|0.9|, P < 0.05). Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment results showed that differentially expressed genes (DEGs) were mainly enriched in the Starch and sucrose metabolism, Plant hormone signal transduction, Ribosome and Glutathione metabolism pathways. Seven hub genes were also retrieved, including Cluster-38255.67514 and Cluster-38255.80127, potentially associated with waterlogging tolerance. It is related to the Ribosome pathway and participates in the process of anti-waterlogging regulation. The results of this experiment provide new insights into the response mechanisms of H. compressa to waterlogging stress and a theoretical framework for the effective selection and breeding of waterlogging-tolerant cultivars.

Metabolic modelling reveals increased autonomy and antagonism in type 2 diabetic gut microbiota

Type 2 diabetes (T2D) presents a global health concern, with evidence highlighting the role of the human gut microbiome in metabolic diseases. This study employs metabolic modelling to elucidate changes in host-microbiome interactions in T2D. Glucose levels, diet, 16S sequences and metadata were collected for 1866 individuals. In addition, microbial community models, and ecological interactions were simulated for the gut microbiomes. Our findings revealed a significant decrease in metabolic fluxes provided by the host's diet to the microbiome in T2D patients, accompanied by increased within-community exchanges. Moreover, the diabetic microbiomes shift towards increased exploitative ecological interactions at the expense of collaborative interactions. The reduced microbiome-to-host butyrate flux, along with decreased fluxes of amino acids (including tryptophan), nucleotides, and B vitamins from the host's diet, further highlight the dysregulation in microbial-host interactions in diabetes. In addition, microbiomes of T2D patients exhibit enrichment in energy metabolism, indicative of increased metabolic activity and antagonism. This study sheds light on the increased microbiome autonomy and antagonism accompanying diabetes, and provides candidate metabolic targets for intervention studies and experimental validation.

Exploring the mechanisms of Chaige Kangyi Recipe in treating recurrent pregnancy loss with insulin resistance

Chinese herbal medicine effectively treats recurrent pregnancy loss, though its mechanism is unclear. This study used RStudio 4.3.0 to collect successful cases for cluster analysis, identifying medication patterns and core formulas, and further researching key prescriptions. The prescription is frequently used for recurrent abortion patients with insulin resistance. UPLC-QTOF-MS identified components, and network pharmacology explored key prescription targets in recurrent abortion with insulin resistance, validated by molecular docking and in vitro experiments. Traditional Chinese medicine treatment for 177 recurrent abortion cases and 640 prescriptions was analysed using RStudio 4.3.0 to identify medication patterns. Chaige Kangyi Recipe (CGKYR) active components and targets were obtained from TCMNPAS, and a herb-ingredient-target gene network was constructed using Cytoscape 3.7.2. GeneCards provided RSA target genes, and Cytoscape visualised a drug-disease target PPI network. Metascape software performed GO and KEGG enrichment analyses. UHPLC-MS/MS identified active compounds in core prescriptions, and molecular docking evaluated the therapeutic effects and mechanisms of major chemical components on key targets. Key prescriptions were derived from RStudio 4.3.0 cluster analysis of the Chaige Kangyi Recipe (CGKYR), commonly used for recurrent miscarriages with insulin resistance. Sixty-seven active ingredients were identified via UPLC-QTOF-MS. Network pharmacology revealed 179 target genes related to CGKYR's effects on recurrent miscarriage with insulin resistance. PPI analysis indicated IL-6, AKT1, STAT3, and INS as potential targets. Molecular docking demonstrated strong binding activity of four compounds with IL-6.CCK-8 assays showed CGKYR promoted HDSC proliferation dose-dependently. In vitro experiments indicated CGKYR increased IL-6 mRNA expression in human decidual stromal cells. CGKYR employs a multifaceted therapy for RPL complicated by insulin resistance, enhancing endometrial receptivity and stimulating HDSC proliferation by upregulating IL-6 mRNA expression in human decidual stromal cells.

Correlation between Lactobacillus of Vaginal Microbiota and the Pregnancy Outcomes for Patients Experiencing Recurrent Miscarriage

The etiology of recurrent miscarriage (RM) is complex, with the vaginal microbiota (VM) being an important factor associated with RM. We aimed to establish the VM composition in both patients with RM and healthy women and further investigate relationship between the subsequent pregnancy outcomes of patients with RM and VM, to explain the potential mechanism of VM in RM to some extent. A cohort study compared the VM between 34 patients with RM and 15 healthy women using a sequencing technique based on Type IIB restriction enzymes for the microbiome (2bRAD-M). Further comparison was made between 11 patients with clinical miscarriages (CM) and 13 patients with ongoing pregnancies (OP) in the RM group who conceived naturally. To determine the VM composition, the 2bRAD-M library was prepared, and sequence and bioinformatics analyses were conducted. The composition of the VM exhibited notable differences between the non-RM and RM groups, with significant findings for alpha diversity (p < 0.05) and beta diversity (p = 0.01). Further analysis between the RM-OP and RM-CM groups revealed a significant difference in Lactobacillus (97.81% ± 2.71% vs. 53.37% ± 46.42%, p = 0.03). Other uncommon species, such as Cutibacterium acnes (C. acnes) (p = 0.04) were found significantly increase in the RM-CM group. Functional annotation analysis revealed 47 related signaling pathways between the two groups. The results of this study indicate that Lactobacillus is associated with subsequent miscarriages and that C. acnes is closely related to pregnancy outcomes of patients with RM.

Significance of miR-1290 in glioblastoma patients with epilepsy

Epilepsy is often the initial symptom in two-thirds of glioblastoma (GBM) patients. Existing studies have shown that microRNAs (miRNAs) play a crucial role in epilepsy. However, their role in epilepsy associated with glioblastoma remains unclear. The aim of our study was to investigate the correlation between miR-1290 expression in GBM patients and pre-operative seizures, as well as patient outcomes. 81 GBM patients were enrolled in our study, and an independent validation was carried out with 92 similar cases. MiRNA profiling of the 81 patients was conducted to identify differentially expressed miRNAs. In the validation cohort, key miRNAs were validated by using quantitative reverse transcriptase polymerase chain reaction (q-PCR). Additionally, functional analysis of these miRNAs was performed through Gene Ontology (GO) analysis. Our array analysis disclosed that there were seven under-expressed miRNAs in patients with preoperative seizures when compared to those without preoperative seizures. Among them, miR-1290 showed the highest fold change. Validation in an independent cohort verified that patients with favorable seizure outcomes had higher miR-1290 expression levels. Functional enrichment analysis demonstrated that the gene expression profiles associated with miR-1290 were enriched in biological processes related to transcription and cell cycle regulation, especially the functions mediated by RNA polymerase II. MiR-1290 emerges as a promising biomarker for predicting seizure susceptibility and overall survival in GBM patients. A specific evaluation of miR-1290 may lead to targeted diagnostic and therapeutic interventions, potentially providing novel strategies for enhancing patient outcomes.

Characterization of lactylation-based phenotypes and molecular biomarkers in sepsis-associated acute respiratory distress syndrome

Sepsis-associated acute respiratory distress syndrome (ARDS) is a heterogeneous disease with high morbidity and mortality. Lactylation plays a crucial role in sepsis and sepsis-induced lung injury. This study aimed to identify distinct lactylation-based phenotypes in patients with sepsis-associated ARDS and determine relevant molecular biomarkers. We analyzed blood transcriptome and clinical data from patients with sepsis-associated ARDS and calculated the lactylation activity. KEGG pathway analysis, drug sensitivity prediction, and immune cell infiltration analysis were performed. Candidate molecular biomarkers were identified by intersecting the feature genes extracted from four machine learning models. Lactylation activity showed significant heterogeneity among patients with sepsis-associated ARDS, which enabled the classification into low- and high-lactylation activity phenotypes. Patients with high-lactylation experienced longer hospital stays and higher mortality rates, as well as distinct signaling pathways, drug responses, and circulating immune cell abundances. Six key markers (ALDOB, CCT5, EP300, PFKP, PPIA, and SIRT1) were identified to differentiate the two lactylation activity phenotypes, all significantly correlated with circulating immune cell populations. This study revealed significant heterogeneity in lactylation activity phenotypes among patients with sepsis-associated ARDS and identified potential biomarkers to facilitate the application of these phenotypes in clinical practice.

Targeting GSK3β and signaling pathways in breast cancer: role of individual members of miR- 23/24/27 cluster

BACKGROUND

The high mortality rate of breast cancer and the difficulties associated with therapeutic resistance, especially in cases where targeted treatments are unavailable, make it a serious threat to women's health. This study examines the relationship between three mature microRNAs (miRNAs) that are clustered together, namely miR- 23a, miR- 27a, and miR- 24-2, as well as their potential correlation with breast cancer.

METHODS

We identified common gene targets of miR- 23a, miR- 27a, and miR- 24-2 using computational analysis. We also checked for the levels of miR- 23a, miR- 27a, and miR- 24-2 in 26 breast tumor tissues (with their matched control) as well as MCF7 and MDA-MB- 231 cell lines using qRT-PCR. Dual-luciferase reporter assay was conducted to validate the binding site of the microRNAs in their target gene. Western blot was performed to study the expression of various breast cancer related genes in the presence of the three microRNAs. In addition, the effect of microRNAs in cancer cell metastasis and cell division was carried out using invasion and cell cycle assay.

RESULTS

Computational analysis identified key genes, including GSK3β, NCOA1 and SP1, which are functionally linked to tumor progression and various other malignancies. All three microRNAs were found to be significantly downregulated in the breast cancer tissue samples in comparison to their respective controls. Kaplan-Meier plot analysis revealed that the expression levels of these genes and associated microRNAs correlates with breast cancer patient survival rates. Reduced SP1 and NCOA1 levels predicted a worse prognosis, but elevated levels of GSK3β were linked with decreased survival. Moreover, miR- 23a and miR- 24-2 specifically target GSK3β, potentially disrupting the Wnt/β-catenin pathway involved in breast cancer development. Functional tests showed that miR- 23a, miR- 27a and miR- 24-2 affect expression of EMT related genes, influencing cell invasion and migration, impacting ERK signaling and EMT, critical in the spread of breast cancer.

CONCLUSION

This study unlocks the potential of targeting the microRNA cluster as a therapeutic approach and emphasizes the complex regulatory roles of each individual members of the miR- 23a/27a/24-2 cluster in the pathogenesis of breast cancer.

Uncovering mechanism of hepatotoxicity diseases caused by tetrahydrocannabinol based on novel network toxicology and experimental verification

This study systematically investigated the molecular mechanisms underlying tetrahydrocannabinol (THC)-induced hepatotoxicity in humans through an integrated approach combining network toxicology, molecular docking, and experimental validation. Our analysis identified 22 core targets associated with THC-mediated hepatotoxicity. Protein-protein interaction (PPI) network analysis revealed significant functional associations among these 22 potential target proteins. KEGG pathway and GO term analyses demonstrated that THC potentially exerts hepatotoxic effects through multiple biological processes, including endocrine resistance, bile secretion, negative regulation of apoptosis, and cellular oxidant detoxification. Disease enrichment analysis further identified several pathological conditions closely associated with THC-induced hepatic damage. Molecular docking simulations demonstrated strong binding affinities between THC and functional domains of 17 target proteins that participated in the aforementioned enriched pathways. An in vitro model of THC-induced hepatocyte injury was successfully established and subsequently validated through RT-qPCR experiment. THC exposure significantly altered the expression patterns of 10 critical target genes: ERBB2, GPX1, MAPK14, NR1H4, SOD1, CXCR2, PPARG, EGFR, TYMS and KDR. The hepatotoxic effects of THC appear to arise from the synergistic interplay of multiple pathways and the coordinated dysfunction of various gene products. These findings elucidate key molecular pathways and therapeutic targets associated with THC-induced hepatotoxicity, providing a theoretical foundation for developing clinical interventions and hepatoprotective strategies against cannabis-related liver damage.

Differential changes in the microglial transcriptome between neonatal and adult mice after spinal cord injury

Spinal cord injury (SCI) remains a significant therapeutic challenge, lacking effective treatment options. Related studies have found that neonatal microglia are more effective than adult microglia in promoting the recovery of SCI, but the reason why neonatal, not adult, microglia are more conducive to SCI recovery is not clear, the differences of gene expression and pathways between them are still worth exploring. Therefore, we examined changes in the microglial transcriptome after SCI in neonatal and adult mice. We identified hub genes or pathways that exhibited significant differential expression between the two groups. Four Gene sets were established for further analysis, named Gene set 1, Gene set 2, Gene set 3, Gene set 4, respectively. GO analysis revealed enrichment in categories critical for injury repair, including DNA metabolism, replication, recombination, meiotic cell cycle progression, regulation of cell-cell adhesion, megakaryocyte and endothelial development, modulation of the neuroinflammatory response, endocytosis, and regulation of cytokine production and cell migration. KEGG analysis revealed enrichment in pathways critical for various cellular processes, including the p53, TNF, PI3K-AKT, PPAR and B cell receptor signaling pathway, axon guidance, cytokine-cytokine receptor interaction. PPI and TF-hub gene-microRNA networks were constructed to elucidate the underlying gene regulatory mechanisms. Additionally, drug prediction was performed to identify potential therapeutic candidates. Finally, 11 hub genes (Chek1, RRM2, Lyve1, Mboat1, Clec4a3, Ccnd1, Cdk6, Zeb1, Igf1, Pparg, and Cd163) were selected from four Gene sets for further validation using qRT-PCR. We identified candidate genes and pathways involved in microglial transcriptome heterogeneity after SCI in neonatal and adult mice. These findings provide valuable insights into potential therapeutic targets for neonatal microglia in the treatment of SCI.

Mass spectrometry-based characterisation of the cardiac microtissue metabolome and lipidome

INTRODUCTION

The use of large, non-sample specific metabolite reference libraries often results in high proportions of false positive annotations in untargeted metabolomics.

OBJECTIVE

This study aimed to measure and curate a library of polar metabolites and lipids present in cardiac microtissues.

RESULTS

Untargeted ultra-high performance liquid chromatography-coupled mass spectrometry measurements of cardiac microtissue intracellular extracts were annotated by comparison against four spectral databases and a retention time library. The annotations were combined to create a library of 313 polar metabolites and 1004 lipids.

CONCLUSIONS

The curated library will facilitate higher confidence metabolite annotation in mass spectrometry-based untargeted metabolomics of cardiac microtissues.

Learning motif features and topological structure of molecules for metabolic pathway prediction

Metabolites serve as crucial biomarkers for assessing disease progression and understanding underlying pathogenic mechanisms. However, when the metabolic pathway category of metabolites is unknown, researchers face challenges in conducting metabolomic analyses. Due to the complexity of wet laboratory experimentation for pathway identification, there is a growing demand for predictive methods. Various computational approaches, including machine learning and graph neural networks, have been proposed; however, interpretability remains a challenge. We have developed a neural network framework called MotifMol3D, which is designed for predicting molecular metabolic pathway categories. This framework introduces motif information to mine local features of small-sample molecules, combining with graph neural network and 3D information to complete the prediction task. Using a dataset of 5,698 molecules that participate in 11 metabolic pathway categories in the KEGG database, MotifMol3D outperformed state-of-the-art methods in precision, recall, and F1 score. In addition, ablation study and motif analysis have demonstrated the effectiveness and usefulness of the model. Motif analysis, in particular, has shown motif information can actually characterize the main features of specific pathway molecules to a certain extent and enhance the interpretability of the model. An external validation further corroborates this observation. MotifMol3D is an open-source tool that is available at https://github.com/Irena-Zhang/MotifMol3D.git .Scientific contribution MotifMol3D integrates motif information, graph neural networks, and 3D structural data to enhance feature extraction for small-sample molecules, improving the precision and interpretability of metabolic pathway predictions. The model outperforms state-of-the-art approaches in precision, recall, and F1 score. This work reveals how motif information characterizes pathway-specific molecules, offering novel insights into molecular properties within metabolic pathways.

Chromosome-level genome assembly and annotation of Chinese herring (Ilisha elongata)

The Chinese herring (Ilisha elongata) is a commercially and scientifically significant fish species. In this study, we conducted high-precision whole-genome sequencing using two high-throughput platforms: second-generation MGI and third-generation PacBio. Hi-C technology assisted in assembling the contig sequences onto 24 chromosomes, resulting in a high-quality chromosome-level genome map with excellent continuity and coverage. The completed genome size was approximately 815 Mb, with a contig N50 of 4.82 Mb, scaffold N50 of 32.61 Mb, and a chromosome mounting rate of 95.32%. SNP and InDel purity rates were 0.003% and 0.012%, respectively, and the genome assembly completeness was 96.68%, assessed by BUSCO. Repetitive sequences were annotated via ab initio and homology predictions, identifying 295.7 Mb of repetitive sequences, constituting 35.08% of the genome. A total of 26,381 protein-coding genes were predicted, with 24,596 functionally annotated.

Serum metabolomics to identify molecular subtypes and predict XELOX efficacy in colorectal cancer

Colorectal cancer (CRC) is one of the most common cancers; however, accurately predicting prognosis based on existing molecular subtypes remains challenging. The XELOX regimen, which combines oxaliplatin and capecitabine, is the cornerstone of chemotherapy for CRC treatment. However, there is a notable lack of reliable predictive models for determining the sensitivity of this treatment. This study aimed to establish a novel classification system for CRC and develop a predictive model for XELOX chemotherapeutic sensitivity using serum metabolomics. We recruited 89 patients with CRC and 89 age- and sex-matched healthy controls for untargeted metabolomic studies to identify tumor-specific serum metabolites. The patients were grouped into distinct metabolic subtypes using unsupervised clustering. A serum metabolite combination predictive of the efficacy of XELOX was established using Cox regression analysis in 34 patients with stage III CRC. Using unsupervised clustering based on the serum metabolites, three distinct clusters were identified. Notably, Cluster 3, which was characterized by uniform lipid and amino acid levels, demonstrated the best prognosis. Our analysis revealed that D-glucose 6-phosphate, presqualene diphosphate, and leukotriene B4 levels were negatively correlated with XELOX sensitivity, whereas 15-HETE and N-acetyl-l-methionine levels were positively correlated. Based on these findings, we constructed a predictive model validated in an independent cohort of 34 patients with stage III CRC. In summary, this study identified a novel classification of CRC based on serum metabolites and developed a potential prognostic model for XELOX chemotherapeutic efficacy, which may have direct effects on the treatment and prognosis of CRC.

Deciphering the relationship between sarcopenia and aging: A combined text mining and bioinformatics approach

AIM

Sarcopenia is recognized as an age-related muscle disease, but there has been no comprehensive analysis of what is different between normal aging and sarcopenia, with an awareness of the worldwide research to date. Therefore, in this study, we used text mining of PubMed articles on sarcopenia and focused our bioinformatics analysis on the items that have been identified.

METHODS

This study compared gene-level changes in sarcopenia and normal aging to identify sarcopenia-specific gene changes using high-throughput sequencing data. In particular, text mining analysis was used to identify pathways and mechanisms of interest in sarcopenia research, and focus more on these mechanisms.

RESULTS

We identified the pathways common to sarcopenia and normal aging. Interleukin-7 pathways were associated with both conditions. Although changes in phagosome-related pathways were suggested as sarcopenia-specific, no significant changes in phagosome formation, lysosome-related and mitophagy-related gene groups were identified. However, genes in the nicotinamide adenine dinucleotide phosphate oxidase catalytic subunit family were shown to be possibly altered, suggesting the involvement of oxidative stress regulatory pathways.

CONCLUSIONS

A comprehensive bioinformatics analysis, complemented by the text mining of the extant literature, suggested that sarcopenia might not be characterized by a failure of autophagy as a whole, but rather, by a disruption of oxidative stress regulation, particularly nicotinamide adenine dinucleotide phosphate oxidase catalytic subunit-related pathways at a subsequent stage of autophagy after phagosome-lysosome fusion. Geriatr Gerontol Int 2025; ••: ••-••.

The carRS-ompV-virK operon of Vibrio cholerae senses antimicrobial peptides and activates the expression of multiple resistance systems

Antimicrobial peptides are small cationic molecules produced by eukaryotic cells to combat infection, as well as by bacteria for niche competition. Polymyxin B (PmB), a cyclic antimicrobial peptide, is used prophylactically in livestock and as a last-resort treatment for multidrug-resistant bacterial infections in humans. In this study, a transcriptomic analysis in Vibrio cholerae showed that expression of the uncharacterized gene ompV is stimulated in response to PmB. We found that ompV is organized in a conserved four-gene operon with the two-component system carRS and virK in V. cholerae. A virK deletion mutant and an ompV deletion mutant were more sensitive to antimicrobials, suggesting that both OmpV and VirK contribute to antimicrobial resistance. Our transcriptomic analysis showed that the efflux pump vexAB, a known effector of PmB resistance, was upregulated in an ompV-dependent manner in the presence of PmB. The predicted structure of OmpV revealed a lateral opening in the β-barrel wall with access to an electronegative pocket in the barrel lumen that can accommodate PmB. Such an interaction could facilitate intracellular signaling through a conformational change in OmpV. This provides the first evidence of a specialized operon governing multiple systems for antimicrobial resistance in V. cholerae.

Developmental pathways underlying sexual differentiation in the U/V sex chromosome system of giant kelp

In many multicellular organisms, sexual development is not determined by XX/XY or ZW/ZZ systems but by U/V sex chromosomes. In U/V systems, sex determination occurs in the haploid phase, with U chromosomes in females and V chromosomes in males. Here, we explore several male, female, and partially sex-reversed male lines of giant kelp to decipher how U/V sex chromosomes and autosomes initiate male versus female development. We identify a key set of genes on the sex chromosomes involved in triggering sexual development and characterize autosomal effector genes underlying sexual differentiation. We show that male, but not female, development involves large-scale transcriptome reorganization with pervasive enrichment in regulatory genes, faster evolutionary rates, and high species-specificity of male-biased genes. Our observations imply that a female-like phenotype is the "ground state", which is complemented by the presence of a U-chromosome but overridden by a dominant male developmental program triggered by the V-chromosome.

Integrated multiomics analysis identifies potential biomarkers and therapeutic targets for autophagy associated AKI to CKD transition

This study explored the relationship between acute kidney injury (AKI) and chronic kidney disease (CKD), focusing on autophagy-related genes and their immune infiltration during the transition from AKI to CKD. We performed weighted correlation network analysis (WGCNA) using two microarray datasets (GSE139061 and GSE66494) in the GEO database and identified autophagy signatures by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA enrichment analysis. Machine learning algorithms such as LASSO, random forest, and XGBoost were used to construct the diagnostic model, and the diagnostic performance of GSE30718 (AKI) and GSE37171 (CKD) was used as validation cohorts to evaluate its diagnostic performance. The study identified 14 autophagy candidate genes, among which ATP6V1C1 and COPA were identified as key biomarkers that were able to effectively distinguish between AKI and CKD. Immune cell infiltration and GSEA analysis revealed immune dysregulation in AKI, and these genes were associated with inflammation and immune pathways. Single-cell analysis showed that ATP6V1C1 and COPA were specifically expressed in AKI and CKD, which may be related to renal fibrosis. In addition, drug prediction and molecular docking analysis proposed SZ(+)-(S)-202-791 and PDE4 inhibitor 16 as potential therapeutic agents. In summary, this study provides new insights into the relationship between AKI and CKD and lays a foundation for the development of new treatment strategies.

Integrated bioinformatic analysis of immune infiltration and disulfidptosis related gene subgroups in type A aortic dissection

Type A aortic dissection (TAAD) is a lethal cardiovascular disease characterized by the separation of the layers within the aortic wall. The underlying pathological mechanisms of TAAD requires further elucidation to develop effective prevention and pharmacological treatment strategies. Inflammation plays a crucial role in TAAD pathogenesis. Disulfidptosis, an emerging type of cell death, may shed light on disease mechanisms. This study investigates the role of disulfidptosis-related genes in immune infiltration in TAAD. TAAD gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. Immune cell infiltration analysis assessed immune cell dysregulation in TAAD. Differentially expressed genes (DEGs) between TAAD samples and controls were identified and intersected with known disulfidptosis-related gene sets to obtain relevant DEGs. Hub genes were identified using machine learning algorithms. A diagnostic model was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression on 25 TAAD samples. Consensus clustering classified TAAD samples based on disulfidptosis-related gene expression. Functional enrichment analyses, including Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, elucidated associated biological processes and pathways. A total of 13,316 DEGs were identified, among which 11 disulfidptosis-related genes were screened: INF2, CD2AP, PDLIM1, ACTN4, MYH10, MYH9, FLNA, FLNB, TLN1, MYL6, ACTB, CAPZB, DSTN, and IQGAP1. Most of these genes exhibited lower expression levels in TAAD samples, except CAPZB, and were correlated with immune cell infiltration. Cluster-specific DEGs were found in one cluster, involving several immune response processes. Co-clustering analysis based on disulfidptosis-related genes classified TAAD samples into two clusters, with higher gene expression levels observed in cluster C2 compared to cluster C1. Three key hub genes were identified, and potential therapeutic mechanisms for TAAD were explored. Immuno-infiltration results revealed significant differences in immune profiles, with higher immunological scores and more extensive immune infiltration in TAAD. Disulfidptosis occurs in TAAD and is associated with immune cell infiltration and metabolic activity, influencing immune cell function and responses. These findings suggest that disulfidptosis may promote TAAD progression through the induction of immune responses and metabolic activities. This research provides new insights into the pathogenesis and identifies potential therapeutic targets for TAAD.

Enhancing caproate production in lactate-containing effluents by Megasphaera hexanoica through acetate modulation

The anaerobic bacterium Megasphaera hexanoica has great potential for caproate production through reverse β-oxidation using lactate, acetate, and butyrate as substrates. This study assessed the effect of acetate concentrations (10-50 mM) on caproate biosynthesis, lactate oxidation, and cell synthesis in M. hexanoica. At 30 mM acetate, caproate production reached 42.83 mM, with an electron efficiency of 67.03 % and a specific productivity of 4.47 gCA·h-1·gDCW-1. Subsequent fed-batch experiments with lactate, acetate, and butyrate maintained continuous caproate production, achieving 65.21 mM. Bioreactor assays further validated the strategy, yielding 65.25 mM caproate over 180 h. Mechanistic analysis demonstrated that 30 mM acetate optimized acetyl-CoA flux and enhanced caproyl-CoA transferase activity (3.5 U‧mg-1), supporting caproate synthesis. Kinetic modeling demonstrated the Logistic model fit lactate consumption (R2 = 0.991), while the Fitzhugh model captured caproate production (R2 = 0.991, NRMSE = 1.137). The findings offer practical insights for industrial-scale caproate production.

Antimicrobial effects, and selection for AMR by non-antibiotic drugs in a wastewater bacterial community

Antimicrobial resistance (AMR) is a major threat to human, animal, and crop health. AMR can be directly selected for by antibiotics, and indirectly co-selected for by biocides and metals, at environmentally relevant concentrations. Some evidence suggests that non-antibiotic drugs (NADs) can co-select for AMR, but previous work focused on exposing single model bacterial species to predominately high concentrations of NADs. There is a significant knowledge gap in understanding a range of NAD concentrations, (including lower µg/L concentrations found in the environment) on mixed bacterial communities containing a diverse mobile resistome. Here, we determined the antimicrobial effect and selective potential of diclofenac, metformin, and 17-β-estradiol, NADs that are commonly found environmental pollutants, in a complex bacterial community using a combination of culture based, metagenome, and metratranscriptome approaches. We found that diclofenac, metformin, and 17-β-estradiol at 50 µg/L, 26 µg/L, and 24 µg/L respectively, significantly reduced growth of a bacterial community although only 17-β-estradiol selected for an AMR marker using qPCR (from 7 µg/L to 5400 µg/L). Whole metagenome sequencing indicated that there was no clear selection by NADs for antibiotic resistance genes, or effects on community composition. Additionally, increases in relative abundance of some specific metal resistance genes (such as arsB) were observed after exposure to diclofenac, metformin, and 17-β-estradiol. These results indicate that environmentally relevant concentrations of NADs are likely to affect community growth, function, and potentially selection for specific metal resistance genes.

Disulfidptosis-related LncRNAs forecast the prognosis of acute myeloid leukemia

Acute myeloid leukemia (AML) is a highly aggressive hematologic malignancy with a poor prognosis for patients. Disulfidptosis response-related long non-coding RNAs (DRLs) have been demonstrated to be closely associated with cancer development. Therefore, this study aims to construct a prognostic DRL signature and investigate the immune microenvironment for AML. RNA-seq and clinical data for AML patients were obtained from The Cancer Genome Atlas (TCGA) database. A total of 344 disulfidptosis-associated lncRNAs were identified, and a prognostic model consisting of seven lncRNAs was constructed and validated. Two risk groups, high-risk and low-risk, were identified. The model demonstrated a robust capacity to predict prognosis, with a worse overall survival for patients in the high-risk group. Additionally, differential expression of the seven lncRNAs were relatively higher in AML samples than in control samples via quantitative polymerase chain reaction(qPCR). The Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration analysis revealed a substantial infiltration of immune cells and enrichment of immune pathways in the high-risk group. The sensitivity of AML patients to drugs varied according to their risk grade. This study identified a DRL signature, which can effectively predict the prognosis of AML and better understand the mechanism of disulfidptosis in AML. This provides a basis for personalized immunotherapy in AML patients.

miR-143-3p Promotes TSCM Differentiation and Inhibits Progressive T Cell Differentiation via Inhibiting ABL2 and PAG1

BACKGROUND

Adoptive cell therapy (ACT), including CAR-T and TCR-T therapies, shows promise for cancer treatment, depending on infused T cell expansion, persistence and activity. We previously characterized four T-cell subsets (TN, TSCM, TCM and TEM) and their miRNA profiles.

OBJECTIVES

This study investigates miR-143-3p's role in T cell differentiation.

METHODS

Using qPCR, we analyzed miR-143-3p expression. Target genes were validated by dual-luciferase assays. Functional assays assessed differentiation markers, proliferation, apoptosis and cytokine secretion.

RESULTS

miR-143-3p was upregulated in early-differentiated TSCM but downregulated during progression. We confirmed ABL2 and PAG1 as direct targets suppressed by miR-143-3p. Overexpression increased early markers (LEF1, CCR7 and CD62L) while decreasing late markers (EOMES, KLRG1 and CD45RO). It also enhanced proliferation, reduced apoptosis and suppressed cytokine secretion.

CONCLUSIONS

miR-143-3p promotes TSCM differentiation and inhibits progressive differentiation by targeting ABL2/PAG1, suggesting new ACT optimization strategies.

Transcriptomic Profiling Reveals Regulatory Pathways of Tomato in Resistance to Verticillium Wilt Triggered by VdR3e

Tomatoes are important horticultural crops worldwide. Verticillium wilt is a disease caused by Verticillium dahliae that causes serious tomato yield losses. V. dahliae can be classified into three distinct races in tomatoes. We identified the specific VdR3e gene of V. dahliae race 3 and found that VdR3e triggered immune responses in the resistant tomato cultivar IVF6384. We confirmed that VdR3e triggers immune responses in the parents of IVF6384 plants and conducted transcriptome sequencing between male and female IVF6384 plants after VdR3e infiltration to analyze the potential regulatory network response to VdR3e. We found that both parents had a series of detoxification and stress resistance responses to VdR3e, but those of the male IVF6384 parent were concentrated in disease resistance-related signaling pathways. Moreover, several vital differentially expressed genes involved in functional annotation related to plant-pathogen interactions and plant hormone signaling stimulated immune responses in Nicotiana benthamiana. This study provides a new and comprehensive perspective on tomato resistance to Verticillium wilt.

Integrative bioinformatics analysis reveals STAT1, ORC2, and GTF2B as critical biomarkers in lupus nephritis with Monkeypox virus infection

The monkeypox virus (MPXV) is currently spreading rapidly around the world, but the mechanisms by which it interacts with lupus nephritis (LN) are unknown. The aim of this study was to investigate the role and mechanism of lupus nephritis combined with monkeypox virus infection. The data comes from GEO and GeneCards.Through Limma and Weighted Gene Co-expression Network Analysis (WGCNA) analysis, differential expression genes (DEGs) and module genes were identified, and KEGG and GO enrichment analysis was carried out.In addition, a protein-protein interaction (PPI) network was constructed and LASSO regression was used to screen genes related to senescence. The diagnostic effectiveness was evaluated using a Nomogram and the receiver operating characteristic (ROC) curve and verified using GSE99967.Immune infiltration and gene set enrichment analysis (GSEA) Were also included in the study.In the end, miRNet was used to construct a miRNA-mRNA-TF network and screen targeted drugs through DGIdb. 5707 DEGs were identified in the lupus nephritis and 737 in the monkeypox data. WGCNA and Lasso regression analyses screened for three important targets (STAT1, ORC2, and GTF2B) .Predictive modeling and ROC of STAT1, ORC2 and GTF2B by Nomogram showed good diagnostic value .Immune infiltration analysis showed immune cell disorders and related pathway activation.The miRNA-mRNA-TF network covers 516 miRNAs and 15 transcription factors, and enrichment analysis shows that it plays an important role in senescence and inflammation.Potential Target Drugs Screened Include Guttiferone K And Silicon Phthalocyanine 4. This study identifies STAT1, ORC2, and GTF2B as key factors in cellular senescence and immune dysregulation associated with lupus nephritis and monkeypox infection, suggesting they may serve as important predictive targets.

PPI networking, in-vitro expression analysis, virtual screening, DFT, and molecular dynamics for identifying natural TNF-α inhibitors for rheumatoid arthritis

In humans, rheumatoid arthritis (RA) is a deadly autoimmune disease that affects bone health. Although the specific etiology of RA is unknown, scientific evidence suggests that smoking, genetic abnormalities, and environmental factors may all contribute to the disease's progression. We employed protein-protein interaction (PPI) networking analysis to identify a possible therapeutic target for RA. The lead-like molecule for the selected target was then found via virtual screening in the Indian medicinal plants phytochemistry and therapeutics database. Molecular dynamics has confirmed the stability of drug target-lead-like molecule complexes. The networking analysis identifies TNF-α as a potential therapeutic target for RA. TNF-α expression was verified using in vitro studies. Cassamedine was identified as a possible lead molecule among 17,967 chemicals in the Indian Medicinal Plants Phytochemistry and Therapeutics database using virtual screening experiments. The molecular docking results of the lead compound interaction with TNF-α were clarified by the quantum mechanism (QM) technique, namely, density functional theory (DFT). The stability of the lead-like compound with TNF-α was confirmed using 200 ns of molecular dynamics simulations. Energy calculations using molecular mechanics Poisson-Boltzmann surface area (MMPBSA) confirm the free energy between TNF-α and lead-like molecules.

Investigation of PANoptosis pathway in age-related macular degeneration triggered by Aβ1-40

Our study aimed to identify PANoptosis in Aβ1-40-induced AMD, both in vivo and in vitro, and to determine if AIM2-PANoptosome mediates this process. We used transcriptomics to explore the signaling pathways and target genes linked to PANoptosis within a mouse model of AMD triggered by Aβ1-40. Optical coherence tomography (OCT), hematoxylin and eosin (H&E) staining, and electroretinography (ERG) were employed to assess retinal damage in terms of morphology and function. Morphological changes in ARPE-19 cells were observed using optical microscopy and scanning electron microscopy. Enzyme-linked immunosorbent assay (ELISA) was used to detect the levels of cytokines in cell supernatants, mouse orbital serum, and human plasma to evaluate the severity of inflammation. CO-immunoprecipitation(CoIP) and molecular docking were performed to assess the impact and expression of proteins associated with the AIM2-PANoptosome. Quantitative polymerase chain reaction (qPCR), Western blot (WB), immunofluorescence, and apoptosis detection kits were used to evaluate the expression levels of genes and proteins related to PANoptosis-like cell death. Our results showed that the Aβ1-40-induced AMD model had increased expression of apoptosis, necroptosis, and pyroptosis pathways, and AIM2-PANoptosome components. CoIP and docking confirmed increased AIM2, ZBP1, and PYRIN levels under Aβ1-40 treatment. WB and immunofluorescence showed upregulation of PANoptosis-related proteins. Inhibitors reduced Aβ-induced protein expression. ELISA showed increased inflammatory cytokines. Apoptosis assays and microscopy revealed Aβ1-40-induced ARPE-19 cell loss and morphological changes. In conclusion, the Aβ1-40-induced AMD model displayed PANoptosis-like cell death, offering insights into disease pathogenesis.

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning

As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of breast cancer patients. Lactylation-associated subtypes were obtained by unsupervised consensus clustering analysis. Lactylation-related gene signature (LRS) was constructed by 15 machine learning algorithms, and the relationship between LRS and tumor microenvironment (TME) as well as drug sensitivity was analyzed. In addition, the expression of genes in the LRS in different cells was explored by single-cell analysis and spatial transcriptome. The expression levels of genes in LRS in clinical tissues were verified by RT-PCR. Finally, the potential small-molecule compounds were analyzed by CMap, and the molecular docking model of proteins and small-molecule compounds was constructed. LRS was composed of 6 key genes (SHCBP1, SIM2, VGF, GABRQ, SUSD3, and CLIC6). BC patients in the high LRS group had a poorer prognosis and had a TME that promoted tumor progression. Single-cell analysis and spatial transcriptome revealed differential expression of the key genes in different cells. The results of PCR showed that SHCBP1, SIM2, VGF, GABRQ, and SUSD3 were up-regulated in the cancer tissues, whereas CLIC6 was down-regulated in the cancer tissues. Arachidonyltrifluoromethane, AH-6809, W-13, and clofibrate can be used as potential target drugs for SHCBP1, VGF, GABRQ, and SUSD3, respectively. The gene signature we constructed can well predict the prognosis as well as the treatment response of BC patients. In addition, our predicted small-molecule complexes provide an important reference for personalized treatment of breast cancer patients.