Independent organelle and organelle-organelle interactions: essential mechanisms for malignant gynecological cancer cell survival.

Different eukaryotic cell organelles (e.g., mitochondria, endoplasmic reticulum, lysosome) are involved in various cancer processes, by dominating specific cellular activities. Organelles cooperate, such as through contact points, in complex biological activities that help the cell regulate energy metabolism, signal transduction, and membrane dynamics, which influence survival process. Herein, we review the current studies of mechanisms by which mitochondria, endoplasmic reticulum, and lysosome are related to the three major malignant gynecological cancers, and their possible therapeutic interventions and drug targets. We also discuss the similarities and differences of independent organelle and organelle-organelle interactions, and their applications to the respective gynecological cancers; mitochondrial dynamics and energy metabolism, endoplasmic reticulum dysfunction, lysosomal regulation and autophagy, organelle interactions, and organelle regulatory mechanisms of cell death play crucial roles in cancer tumorigenesis, progression, and response to therapy. Finally, we discuss the value of organelle research, its current problems, and its future directions.

Exploring the role of Chinese herbal medicine in the long-term management of postoperative ovarian endometriotic cysts: a systematic review and meta-analysis.

Background: Ovarian endometriotic cysts (OEC) represent the primary manifestation of endometriosis, constituting a hormonally dependent inflammatory disorder in gynecology. It significantly affects the quality of life and reproductive health of women. It is worth noting that traditional Chinese medicine (TCM), especially Chinese herbal medicine (CHM), has been widely applied in mainland China due to its unique therapeutic system and commendable clinical efficacy, bringing new hope for preventing and managing OEC. Objective: This study aims to evaluate the efficacy and safety of CHM in the management of postoperative OEC. Simultaneously, it seeks to explore the medication laws, therapeutic principles, and specific treatment mechanisms of CHM. Methods: Eight electronic databases were searched from their inception to 01 November 2023. Randomized controlled trials (RCTs) assessing the therapeutic effects and safety of CHM for postoperative OEC were included. The risk of bias for each trial was assessed using the Cochrane Collaboration's tool. The certainty of the evidence was evaluated using the GRADE profiler 3.2. Additionally, we extracted formulation from the included studies, conducting a thorough analysis. Results: (ⅰ) Twenty-two RCTs involving 1938 patients were included. In terms of the primary efficacy outcome, the CHM group demonstrated a potentially lower recurrence rate compared to both control (odds ratio (OR) = 0.25; 95% confidence intervals (CI): 0.10-0.64) and conventional western medicine (CWM) (OR = 0.26; 95% CI: 0.11-0.65) groups. Furthermore, the joint application of CHM and CWM resulted in a significant reduction in the recurrence rate (OR = 0.26; 95% CI: 0.17-0.40). (ⅱ) Regarding secondary efficacy outcomes, (a) Total clinical efficacy rate: CHM showcased an augmentation in clinical effectiveness compared to both the control (OR = 4.23; 95% CI: 1.12-15.99) and CWM (OR = 2.94; 95% CI: 1.34-6.43) groups. The combined administration of CHM and CWM substantially enhanced overall clinical effectiveness (OR = 3.44; 95% CI: 2.37-5.00). (b) VAS Score: CHM exhibited the capacity to diminish the VAS score in comparison to surgery alone (Mean difference (MD) = -0.86; 95% CI: -1.01 to -0.71). Nevertheless, no substantial advantage was observed compared to CWM alone (MD = -0.16; 95% CI: -0.49 to 0.17). The integration of CHM with CWM effectively ameliorated pain symptoms (MD = -0.87; 95% CI: -1.10 to -0.65). (c) Serum Level of Cancer antigen 125 (CA125): the CHM group potentially exhibited lower CA125 levels in comparison to CWM alone (MD = -11.08; 95% CI: -21.75 to -0.42). The combined intervention of CHM and CWM significantly decreased CA125 levels (MD = -5.31; 95% CI: -7.27 to -3.36). (d) Pregnancy Rate: CHM exhibited superiority in enhancing the pregnancy rate compared to surgery (OR = 3.95; 95% CI: 1.60-9.74) or CWM alone (OR = 3.31; 95% CI: 1.40-7.83). The combined utilization of CHM and CWM demonstrated the potential to enhance pregnancy rates compared to CWM (OR = 2.99; 95% CI: 1.28-6.98). Concerning safety outcome indicators, CHM effectively decreased the overall incidence of adverse events and, to a certain extent, alleviated perimenopausal symptoms as well as liver function impairment. (ⅲ) Most of CHMs were originated from classical Chinese herbal formulas. Prunus persica (L.) Batsch (Taoren), Angelica sinensis (Oliv.) Diels (Danggui), Salvia miltiorrhiza Bunge (Danshen), Paeonia lactiflora Pall. (Chishao), and Corydalis yanhusuo W.T.Wang (Yanhusuo) were most frequently used CHM. Conclusion: CHM may be a viable choice in the long-term management of postoperative OEC, with the potential to enhance clinical efficacy while decreasing recurrence and adverse effects.

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling.

BACKGROUND: Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets. RESULTS: As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols. CONCLUSIONS: Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

PAnno: A pharmacogenomics annotation tool for clinical genomic testing.

Introduction: Next-generation sequencing (NGS) technologies have been widely used in clinical genomic testing for drug response phenotypes. However, the inherent limitations of short reads make accurate inference of diplotypes still challenging, which may reduce the effectiveness of genotype-guided drug therapy. Methods: An automated Pharmacogenomics Annotation tool (PAnno) was implemented, which reports prescribing recommendations and phenotypes by parsing the germline variant call format (VCF) file from NGS and the population to which the individual belongs. Results: A ranking model dedicated to inferring diplotypes, developed based on the allele (haplotype) definition and population allele frequency, was introduced in PAnno. The predictive performance was validated in comparison with four similar tools using the consensus diplotype data of the Genetic Testing Reference Materials Coordination Program (GeT-RM) as ground truth. An annotation method was proposed to summarize prescribing recommendations and classify drugs into avoid use, use with caution, and routine use, following the recommendations of the Clinical Pharmacogenetics Implementation Consortium (CPIC), etc. It further predicts phenotypes of specific drugs in terms of toxicity, dosage, efficacy, and metabolism by integrating the high-confidence clinical annotations in the Pharmacogenomics Knowledgebase (PharmGKB). PAnno is available at https://github.com/PreMedKB/PAnno. Discussion: PAnno provides an end-to-end clinical pharmacogenomics decision support solution by resolving, annotating, and reporting germline variants.

The Quartet Data Portal: integration of community-wide resources for multiomics quality control.

The Quartet Data Portal facilitates community access to well-characterized reference materials, reference datasets, and related resources established based on a family of four individuals with identical twins from the Quartet Project. Users can request DNA, RNA, protein, and metabolite reference materials, as well as datasets generated across omics, platforms, labs, protocols, and batches. Reproducible analysis tools allow for objective performance assessment of user-submitted data, while interactive visualization tools support rapid exploration of reference datasets. A closed-loop "distribution-collection-evaluation-integration" workflow enables updates and integration of community-contributed multiomics data. Ultimately, this portal helps promote the advancement of reference datasets and multiomics quality control.

Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.

BACKGROUND: Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios. RESULTS: As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies. CONCLUSIONS: Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

Current Research on Complementary and Alternative Medicine in the Treatment of Premature Ovarian Failure: An Update Review.

Complementary and alternative medicine (CAM) encompasses a wide range of different non-mainstream therapies that have been increasingly used for the treatment or adjunct treatment of various ailments, with premature ovarian failure (POF) being one of the most common conditions treated with CAM. This review updates the progress of CAM in the treatment of POF, and we focus specifically on reviewing the evidence for the efficacy and mechanisms of a range of CAM treatments in POF, including single herbal medicines and their active ingredients, compound Chinese medicines, acupuncture and moxibustion, psychotherapy, exercise, vitamins, massage, and dietary supplements. According to the literature, CAM is very helpful for improving POF symptoms, and we hope to provide some instructive suggestions for clinical treatment and experimental research in the future. However, more clinical trials are needed to prove the safety of CAM.