Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond.

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Integration of proteomics and transcriptomics to construct a prognostic signature of renal clear cell carcinoma.

Background: Protein information is often replaced by RNA data in studies to understand cancer-related biological processes or molecular functions, and proteins of prognostic significance in Kidney clear cell carcinoma (KIRC) remain to be mined. Methods: The cancer genome atlas program (TCGA) data was utilized to screen for proteins that are prognostically significant in KIRC. Machine learning algorithms were employed to develop protein prognostic models. Additionally, immune infiltration abundance, somatic mutation differences, and immunotherapeutic responses were analyzed in various protein risk subgroups. Ultimately, the validation of protein-coding genes was confirmed by utilizing an online database and implementing quantitative real-time PCR (qRT-PCR). Results: The patients were divided into two risk categories based on prognostic proteins, and notable disparities in both overall survival (OS) and progression free interval (PFI) were observed between the two groups. The OS was more unfavorable in the high-risk group, and there was a noteworthy disparity in the level of immune infiltration observed between the two groups. In addition, the nomogram showed high accuracy in predicting survival in KIRC patients. Conclusion: In this research, we elucidated the core proteins associated with prognosis in terms of survival prediction, immunotherapeutic response, somatic mutation, and immune microenvironment. Additionally, we have developed a reliable prognostic model with excellent predictive capabilities.

Ferroptosis and EMT: key targets for combating cancer progression and therapy resistance.

Iron-dependent lipid peroxidation causes ferroptosis, a form of regulated cell death. Crucial steps in the formation of ferroptosis include the accumulation of ferrous ions (Fe2+) and lipid peroxidation, of which are controlled by glutathione peroxidase 4 (GPX4). Its crucial role in stopping the spread of cancer has been shown by numerous studies undertaken in the last ten years. Epithelial-mesenchymal transition (EMT) is the process by which epithelial cells acquire mesenchymal characteristics. EMT is connected to carcinogenesis, invasiveness, metastasis, and therapeutic resistance in cancer. It is controlled by a range of internal and external signals and changes the phenotype from epithelial to mesenchymal like. Studies have shown that mesenchymal cancer cells tend to be more ferroptotic than their epithelial counterparts. Drug-resistant cancer cells are more easily killed by inducers of ferroptosis when they undergo EMT. Therefore, understanding the interaction between ferroptosis and EMT will help identify novel cancer treatment targets. In-depth discussion is given to the regulation of ferroptosis, the potential application of EMT in the treatment of cancer, and the relationships between ferroptosis, EMT, and signaling pathways associated with tumors. Invasion, metastasis, and inflammation in cancer all include ferroptosis and EMT. The goal of this review is to provide suggestions for future research and practical guidance for applying ferroptosis and EMT in clinical practice.

Multifaceted role of redox pattern in the tumor immune microenvironment regarding autophagy and apoptosis.

The reversible oxidation-reduction homeostasis mechanism functions as a specific signal transduction system, eliciting related physiological responses. Disruptions to redox homeostasis can have negative consequences, including the potential for cancer development and progression, which are closely linked to a series of redox processes, such as adjustment of reactive oxygen species (ROS) levels and species, changes in antioxidant capacity, and differential effects of ROS on downstream cell fate and immune capacity. The tumor microenvironment (TME) exhibits a complex interplay between immunity and regulatory cell death, especially autophagy and apoptosis, which is crucially regulated by ROS. The present study aims to investigate the mechanism by which multi-source ROS affects apoptosis, autophagy, and the anti-tumor immune response in the TME and the mutual crosstalk between these three processes. Given the intricate role of ROS in controlling cell fate and immunity, we will further examine the relationship between traditional cancer therapy and ROS. It is worth noting that we will discuss some potential ROS-related treatment options for further future studies.

Biological and pharmacological roles of m6A modifications in cancer drug resistance.

Cancer drug resistance represents the main obstacle in cancer treatment. Drug-resistant cancers exhibit complex molecular mechanisms to hit back therapy under pharmacological pressure. As a reversible epigenetic modification, N6-methyladenosine (m6A) RNA modification was regarded to be the most common epigenetic RNA modification. RNA methyltransferases (writers), demethylases (erasers), and m6A-binding proteins (readers) are frequently disordered in several tumors, thus regulating the expression of oncoproteins, enhancing tumorigenesis, cancer proliferation, development, and metastasis. The review elucidated the underlying role of m6A in therapy resistance. Alteration of the m6A modification affected drug efficacy by restructuring multidrug efflux transporters, drug-metabolizing enzymes, and anticancer drug targets. Furthermore, the variation resulted in resistance by regulating DNA damage repair, downstream adaptive response (apoptosis, autophagy, and oncogenic bypass signaling), cell stemness, tumor immune microenvironment, and exosomal non-coding RNA. It is highlighted that several small molecules targeting m6A regulators have shown significant potential for overcoming drug resistance in different cancer categories. Further inhibitors and activators of RNA m6A-modified proteins are expected to provide novel anticancer drugs, delivering the therapeutic potential for addressing the challenge of resistance in clinical resistance.

Smooth muscle cell fate decisions decipher a high-resolution heterogeneity within atherosclerosis molecular subtypes.

BACKGROUND: Mounting evidence has revealed the dynamic variations in the cellular status and phenotype of the smooth muscle cell (SMC) are vital for shaping the atherosclerotic plaque microenvironment and ultimately mapping onto heterogeneous clinical outcomes in coronary artery disease. Currently, the underlying clinical significance of SMC evolutions remains unexplored in atherosclerosis. METHODS: The dissociated cells from diseased segments within the right coronary artery of four cardiac transplant recipients and 1070 bulk samples with atherosclerosis from six bulk cohorts were retrieved. Following the SMC fate trajectory reconstruction, the MOVICS algorithm integrating the nearest template prediction was used to develop a stable and robust molecular classification. Subsequently, multi-dimensional potential biological implications, molecular features, and cell landscape heterogeneity among distinct clusters were decoded. RESULTS: We proposed an SMC cell fate decision signature (SCFDS)-based atherosclerosis stratification system and identified three SCFDS subtypes (C1-C3) with distinguishing features: (i) C1 (DNA-damage repair type), elevated base excision repair (BER), DNA replication, as well as oxidative phosphorylation status. (ii) C2 (immune-activated type), stronger immune activation, hyper-inflammatory state, the complex as well as varied lesion microenvironment, advanced stage, the most severe degree of coronary stenosis severity. (iii) C3 (stromal-rich type), abundant fibrous content, stronger ECM metabolism, immune-suppressed microenvironment. CONCLUSIONS: This study uncovered atherosclerosis complex cellular heterogeneity and a differentiated hierarchy of cell populations underlying SMC. The novel high-resolution stratification system could improve clinical outcomes and facilitate individualized management.

Predicting bladder cancer survival with high accuracy: insights from MAPK pathway-related genes.

The mitogen-activated protein kinase (MAPK) pathway plays a critical role in tumor development and immunotherapy. Nevertheless, additional research is necessary to comprehend the relationship between the MAPK pathway and the prognosis of bladder cancer (BLCA), as well as its influence on the tumor immune microenvironment. To create prognostic models, we screened ten genes associated with the MAPK pathway using COX and least absolute shrinkage and selection operator (LASSO) regression analysis. These models were validated in the Genomic Data Commons (GEO) cohort and further examined for immune infiltration, somatic mutation, and drug sensitivity characteristics. Finally, the findings were validated using The Human Protein Atlas (HPA) database and through Quantitative Real-time PCR (qRT-PCR). Patients were classified into high-risk and low-risk groups based on the prognosis-related genes of the MAPK pathway. The high-risk group had poorer overall survival than the low-risk group and showed increased immune infiltration compared to the low-risk group. Additionally, the nomograms built using the risk scores and clinical factors exhibited high accuracy in predicting the survival of BLCA patients. The prognostic profiling of MAPK pathway-associated genes represents a potent clinical prediction tool, serving as the foundation for precise clinical treatment of BLCA.

Deciphering the prognostic role of endoplasmic reticulum stress in lung adenocarcinoma: integrating prognostic prediction and immunotherapy strategies.

Endoplasmic reticulum stress (ERS) is a critical factor influencing lung adenocarcinoma (LUAD) progression and patient outcomes. In this study, we analyzed gene expression data from LUAD samples sourced from The Cancer Genomic Atlas and Gene Expression Omnibus databases. Utilizing advanced statistical methods including LASSO and Cox regression, we developed a ERS-associated signature (ERAS) based on ten ERS-related genes. This model stratified patients into high- and low-risk groups, with the high-risk group exhibiting decreased survival rates, elevated tumor mutational burden, and heightened chemotherapy sensitivity. Additionally, we observed lower immune and ESTIMATE scores in the high-ERAS group, indicating a potentially compromised immune response. Experimental validation through quantitative real-time polymerase chain reaction confirmed the utility of our model. Furthermore, we constructed a nomogram to predict 1-, 3-, and 5-year survival rates, providing clinicians with a valuable tool for personalized patient management. In conclusion, our study demonstrates the efficacy of the ERAS in identifying high-ERAS LUAD patients, offering promising implications for improved prognostication and treatment strategies.

The underlying mechanism of chimeric antigen receptor (CAR)-T cell therapy triggering secondary T-cell cancers: Mystery of the Sphinx?

The U.S. Food and Drug Administration (FDA) has reported cases of T-cell malignancies, including CAR-positive lymphomas, in patients receiving B cell maturation antigen (BCMA)- or CD19-targeted autologous CAR-T cell immunotherapy. These reports were derived from clinical trials and/or post-marketing adverse event data. This finding has attracted widespread attention. Therefore, it is essential to explore the potential mechanisms by which chimeric antigen receptor (CAR)-T cell therapy triggers secondary T-cell cancers to further guarantee the safety of CAR-T cell therapy.

Non-targeted metabolomics revealed novel links between serum metabolites and primary ovarian insufficiency: a Mendelian randomization study.

Background: Primary ovarian insufficiency (POI) is a common clinical endocrine disorder with a high heterogeneity in both endocrine hormones and etiological phenotypes. However, the etiology of POI remains unclear. Herein, we unraveled the causality of genetically determined metabolites (GDMs) on POI through Mendelian randomization (MR) study with the overarching goal of disclosing underlying mechanisms. Methods: Genetic links with 486 metabolites were retrieved from GWAS data of 7824 European participants as exposures, while GWAS data concerning POI were utilized as the outcome. Via MR analysis, we selected inverse-variance weighted (IVW) method for primary analysis and several additional MR methods (MR-Egger, weighted median, and MR-PRESSO) for sensitivity analyses. MR-Egger intercept and Cochran's Q statistical analysis were conducted to assess potential heterogeneity and pleiotropy. In addition, genetic variations in the key target metabolite were scrutinized further. We conducted replication, meta-analysis, and linkage disequilibrium score regression (LDSC) to reinforce our findings. The MR Steiger test and reverse MR analysis were utilized to assess the robustness of genetic directionality. Furthermore, to deeply explore causality, we performed colocalization analysis and metabolic pathway analysis. Results: Via IVW methods, our study identified 33 metabolites that might exert a causal effect on POI development. X-11437 showed a robustly significant relationship with POI in four MR analysis methods (P IVW=0.0119; P weighted-median =0.0145; PMR-Egger =0.0499; PMR-PRESSO =0.0248). Among the identified metabolites, N-acetylalanine emerged as the most significant in the primary MR analysis using IVW method, reinforcing its pivotal status as a serum biomarker indicative of an elevated POI risk with the most notable P-value (P IVW=0.0007; PMR-PRESSO =0.0022). Multiple analyses were implemented to further demonstrate the reliability and stability of our deduction of causality. Reverse MR analysis did not provide evidence for the causal effects of POI on 33 metabolites. Colocalization analysis revealed that some causal associations between metabolites and POI might be driven by shared genetic variants. Conclusion: By incorporating genomics with metabolomics, this study sought to offer a comprehensive analysis in causal impact of serum metabolome phenotypes on risks of POI with implications for underlying mechanisms, disease screening and prevention.

Prevalence, risk factors, psychological effects of children and adolescents with lower urinary tract symptoms: a large population-based study.

Background: Lower urinary tract symptoms (LUTS) are clinically frequent and seriously affect the psychological and mental health of children and adolescents. However, most studies on LUTS and its influence on the psychological behavior and mental health have focused on adults. This study aimed to investigate LUTS prevalence and associated factors in children and adolescents and explore its impact on psychological behavior. Materials and methods: From October 2019 to November 2021, an epidemiological LUTS survey was carried out on 6,077 children aged 6-15 years old in 12 primary and secondary schools in China by using anonymous questionnaires. Results: A total of 5,500 valid questionnaires were collected, and the total prevalence of four representative symptoms of LUTS: urgency, frequency, daytime urinary incontinence, and nocturnal enuresis was 19.46%, 14.55%, 9.75%, and 8.4%, respectively. The prevalence decreased with age, which decreased rapidly in children aged 6-12 years old. The incidence of LUTS in those who did not continue to use disposable diapers (DD) and began to perform elimination communication (EC) after the age of 1 was significantly higher than that of those who stopped using DD and started EC before 1 year of age (P < 0.05). There were significant differences in the occurrence of LUTS without toiled training (TT) (P < 0.05). The prevalence of LUTS in males was significantly higher than in females (P < 0.05). LUTS in children and adolescents with constipation was significantly higher compared to those without constipation (P < 0.05). The detection rate of abnormal psychological behavior in the LUTS group was 44.6%, which was significantly higher than that in the no LUTS group (21.4%, P < 0.05). The scores of emotional symptoms, conduct problems, hyperactivity, and peer communication problems were significantly higher in the LUTS group than the control group. Conclusions: In Mainland China, the prevalence of LUTS in children and adolescents is high. Continued use of DD after 1 year of age, history of urinary tract infection, lack of TT, and constipation were risk factors for LUTS. EC before 1 year of age is a protective factor for LUTS. The prevalence of psychological behavioral abnormalities is high in children and adolescents with LUTS, which needs to be more concerned.

Emerging role of immunogenic cell death in cancer immunotherapy: Advancing next-generation CAR-T cell immunotherapy by combination.

Immunogenic cell death (ICD) is a stress-driven form of regulated cell death (RCD) in which dying tumor cells' specific signaling pathways are activated to release damage-associated molecular patterns (DAMPs), leading to the robust anti-tumor immune response as well as a reversal of the tumor immune microenvironment from "cold" to "hot". Chimeric antigen receptor (CAR)-T cell therapy, as a landmark in anti-tumor immunotherapy, plays a formidable role in hematologic malignancies but falls short in solid tumors. The Gordian knot of CAR-T cells for solid tumors includes but is not limited to, tumor antigen heterogeneity or absence, physical and immune barriers of tumors. The combination of ICD induction therapy and CAR-T cell immunotherapy is expected to promote the intensive use of CAR-T cell in solid tumors. In this review, we summarize the characteristics of ICD, stress-responsive mechanism, and the synergistic effect of various ICD-based therapies with CAR-T cells to effectively improve anti-tumor capacity.

Omics-based molecular classifications empowering in precision oncology.

BACKGROUND: In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine. CONCLUSION: This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Eravacycline improves the efficacy of anti-PD1 immunotherapy via AP1/CCL5 mediated M1 macrophage polarization in melanoma.

Screening approved library is a promising and safe strategy to overcome the limitation of low response rate and drug resistance in immunotherapy. Accumulating evidence showed that the application of antibiotics has been considered to reduce the effectiveness of anti-PD1 immunotherapy in tumor treatment, however, in this study, an antibiotic drug (Eravacycline, ERV) was identified to improve the efficacy of anti-PD1 immunotherapy in melanoma through screening approved library. Administration of ERV significantly attenuated melanoma cells growth as well as directly or indirectly benefited M1 macrophage polarization. Meanwhile, ERV treatment significantly induced cellular autophagy via damage of mitochondria, leading to up-regulation of ROS production, subsequently, raised CCL5 secretion through elevation AP1 binding to CCL5 promoter via p38 or JNK1/2 activation. Knockdown of Ccl5 expression attenuated ERV triggered M1 macrophage polarization in melanoma cells. Clinical analysis revealed a positive association between high expression of CCL5 and improved prognosis as well as a favorable anti-PD1 therapy in melanoma patients. As expected, application of ERV improved the efficacy of anti-PD1. Overall, our results approved that ERV enhances the efficacy of anti-PD1 immunotherapy in melanoma by promoting the polarization of M1 macrophages, which provided novel therapeutic strategy for improving the effectiveness of melanoma anti-PD1 immunotherapy.

Proteomics appending a complementary dimension to precision oncotherapy.

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

An assessment system for clinical and biological interpretability in ulcerative colitis.

Ulcerative colitis (UC) is a serious inflammatory bowel disease (IBD) with high morbidity and mortality worldwide. As the traditional diagnostic techniques have various limitations in the practice and diagnosis of early ulcerative colitis, it is necessary to develop new diagnostic models from molecular biology to supplement the existing methods. In this study, we developed a machine learning-based synthesis to construct an artificial intelligence diagnostic model for ulcerative colitis, and the correctness of the model is verified using an external independent dataset. According to the significantly expressed genes related to the occurrence of UC in the model, an unsupervised quantitative ulcerative colitis related score (UCRScore) based on principal coordinate analysis was established. The UCRScore is not only highly generalizable across UC bulk cohorts at different stages, but also highly generalizable across single-cell datasets, with the same effect in terms of cell numbers, activation pathways and mechanisms. As an important role of screening genes in disease occurrence, based on connectivity map analysis, 5 potential targeting molecular compounds were identified, which can be used as an additional supplement to the therapeutic of UC. Overall, this study provides a potential tool for differential diagnosis and assessment of bio-pathological changes in UC at the macroscopic level, providing an opportunity to optimize the diagnosis and treatment of UC.

Circadian disruption in cancer hallmarks: novel insight into the molecular mechanisms of tumorigenesis and cancer treatment.

Circadian rhythms are 24-hour rhythms governing temporal organization of behavior and physiology generated by molecular clocks composed of autoregulatory transcription-translation feedback loops (TTFLs). Disruption of circadian rhythms leads to a spectrum of pathologies, including cancer by triggering or being involved in different hallmarks. Clock control of phenotypic plasticity involved in tumorigenesis operates in aberrant dedifferentiating to progenitor-like cell states, generation of cancer stem cells (CSCs) and Epithelial-to-mesenchymal transition (EMT) events. Circadian rhythms might act as candidates for regulatory mechanisms of cellular senescent and functional determinants of senescence-associated secretory phenotype (SASP). Reciprocal control between clock and epigenetics sheds light on post-transcriptional regulation of circadian rhythms and opens avenues for novel anti-cancer strategies. Additionally, disrupting circadian rhythms influences microbiota communities that could be associated with altered homeostasis contributing to cancer development. Herein, we summarize recent advances in support of the nexus between disruptions of circadian rhythms and cancer hallmarks of new dimensions, thus providing novel perspectives on potentially effective treatment approaches for cancer management.

Infiltrating treg reprogramming in the tumor immune microenvironment and its optimization for immunotherapy.

Immunotherapy has shown promising anti-tumor effects across various tumors, yet it encounters challenges from the inhibitory tumor immune microenvironment (TIME). Infiltrating regulatory T cells (Tregs) are important contributors to immunosuppressive TIME, limiting tumor immunosurveillance and blocking effective anti-tumor immune responses. Although depletion or inhibition of systemic Tregs enhances the anti-tumor immunity, autoimmune sequelae have diminished expectations for the approach. Herein, we summarize emerging strategies, specifically targeting tumor-infiltrating (TI)-Tregs, that elevate the capacity of organisms to resist tumors by reprogramming their phenotype. The regulatory mechanisms of Treg reprogramming are also discussed as well as how this knowledge could be utilized to develop novel and effective cancer immunotherapies.

AI hybrid survival assessment for advanced heart failure patients with renal dysfunction.

Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system's robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com , empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling.

A metabolome-wide Mendelian randomization study prioritizes causal circulating metabolites for reproductive disorders including primary ovarian insufficiency, polycystic ovary syndrome, and abnormal spermatozoa.

BACKGROUND: Accumulating studies have highlighted the significant role of circulating metabolomics in the etiology of reproductive system disorders. However, the causal effects between genetically determined metabolites (GDMs) and reproductive diseases, including primary ovarian insufficiency (POI), polycystic ovary syndrome (PCOS), and abnormal spermatozoa (AS), still await thorough clarification. METHODS: With the currently most comprehensive genome-wide association studies (GWAS) data of metabolomics, systematic two-sample Mendelian randomization (MR) analyses were conducted to disclose causal associations between 1,091 blood metabolites and 309 metabolite ratios with reproductive disorders. The inverse-variance weighted (IVW) method served as the primary analysis approach, and multiple effective MR methods were employed as complementary analyses including MR-Egger, weighted median, constrained maximum likelihood (cML-MA), contamination mixture method, robust adjusted profile score (MR-RAPS), and debiased inverse-variance weighted method. Heterogeneity and pleiotropy were assessed via MR-Egger intercept and Cochran's Q statistical analysis. Outliers were detected by Radial MR and MR-PRESSO methods. External replication and metabolic pathway analysis were also conducted. RESULTS: Potential causal associations of 63 GDMs with POI were unearthed, and five metabolites with strong causal links to POI were emphasized. Two metabolic pathways related to the pathogenesis of POI were pinpointed. Suggestive causal effects of 70 GDMs on PCOS were detected, among which 7 metabolites stood out for strong causality with elevated PCOS risk. Four metabolic pathways associated with PCOS mechanisms were recognized. For AS, 64 GDMs as potential predictive biomarkers were identified, particularly highlighting two metabolites for their strong causal connections with AS. Three pathways underneath the AS mechanism were identified. Multiple assessments were conducted to further confirm the reliability and robustness of our causal inferences. CONCLUSION: By extensively assessing the causal implications of circulating GDMs on reproductive system disorders, our study underscores the intricate and pivotal role of metabolomics in reproductive ill-health, laying a theoretical foundation for clinical strategies from metabolic insights.