The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

A multi-omics approach to understand the influence of polyphenols in ovarian cancer for precision nutrition: a mini-review

The impact of polyphenols in ovarian cancer is widely studied observing gene expression, epigenetic alterations, and molecular mechanisms based on new 'omics' technologies. Therefore, the combination of omics technologies with the use of phenolic compounds may represent a promising approach to precision nutrition in cancer. This article provides an updated review involving the current applications of high-throughput technologies in ovarian cancer, the role of dietary polyphenols and their mechanistic effects in ovarian cancer, and the current status and challenges of precision nutrition and their relationship with big data. High-throughput technologies in different omics science can provide relevant information from different facets for identifying biomarkers for diagnosis, prognosis, and selection of specific therapies for personalized treatment. Furthermore, the field of omics sciences can provide a better understanding of the role of polyphenols and their function as signaling molecules in the prevention and treatment of ovarian cancer. Although we observed an increase in the number of investigations, there are several approaches to data acquisition, analysis, and integration that still need to be improved, and the standardization of these practices still needs to be implemented in clinical trials.

Protein Expression and Bioinformatics Study of Granulosa Cells of Polycystic Ovary Syndrome Expressed Under the Influence of DHEA

Background

The reproductive system is heavily dependent on ovarian follicles, which are made up of germ cells (oocytes) and granulosa cells (GCs), including cumulus granulosa cells (CGCs) and mural granulosa cells (MGCs). Understanding their normal and steroid-induced functions is the key to understanding the pathophysiology of endocrinal diseases in women.

Objective

This study investigated the differentially expressed proteins by CGCs and MGCs of patients with polycystic ovarian syndrome (PCOS) and without subsequent exposure to dehydroepiandrosterone sulfate (DHEAS) and functional differentiation.

Design

The present study was observational and experimental study carried out in hospital involving 80 female patients undergoing IVF for infertility.

Methods

In this study, we isolated CGCs and MGCs from the follicular fluid of both PCOS and non-PCOS patients undergoing in vitro fertilization (IVF). The cells were cultured and treated with DHEAS for 48 hours, and these cells were extracted, digested, and analyzed by tandem mass spectrometry followed by processing of the results using open-source bioinformatics tools.

Results

The present investigation discovered 276 and 341 proteins in CGCs and MGCs, respectively. DHEAS reduced the number of proteins expressed by CGCs and MGCs to 34 and 57 from 91 and 94, respectively. Venn results of CGCs revealed 49, 53, 36, and 21 proteins in normal CGCs, PCOS-CGCs, post-DHEAS, and PCOS-CGCs, respectively. Venn analysis of MGCs showed 51 proteins specific to PCOS and 29 shared by normal and PCOS samples after DHEAS therapy. MGCs express the most binding and catalytic proteins, whereas CGCs express transporter-related proteins. A protein pathway study demonstrated considerable differences between normal and PCOS samples, while DHEAS-treated samples of both cell lines showed distinct pathways. String findings identified important network route components such as albumin, actin, apolipoprotein, complement component C3, and heat shock protein.

Conclusion

This is the first study to show how DHEAS-induced stress affects the expression of proteins by MGCs and CGCs isolated from normal and PCOS patients. Further studies are recommended to identify PCOS biomarkers from CGCs and MGCs expressed under the influence of DHEAS.

Pharmacogenomics: Driving Personalized Medicine

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all "-omics" fields (e.g., proteomics, transcriptomics, metabolomics, and metagenomics). This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. Food and Drug Administration approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multicomponent biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues, providing insights into the current status and future direction of health care. SIGNIFICANCE STATEMENT: Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

A Potential Nine-lncRNAs Signature Identification and Nomogram Diagnostic Model Establishment for Papillary Thyroid Cancer

The purpose of our current study was to establish a long non-coding RNA(lncRNA) signature and assess its prognostic and diagnostic power in papillary thyroid cancer (PTC). LncRNA expression profiles were obtained from the Cancer Genome Atlas (TCGA). The key module and hub lncRNAs related to PTC were determined by weighted gene co-expression network analysis (WGCNA) and LASSO Cox regression analyses, respectively. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis were implemented to analyze the possible biological processes and signaling pathways of hub lncRNAs. Associations between key lncRNA expressions and tumor-infiltrating immune cells were identified using the TIMER website, and proportions of immune cells in high/low risk score groups were compared. Kaplan-Meier Plotter was used to evaluate the prognostic significance of hub genes in PTC. A diagnostic model was conducted with logistic regression analysis, and its diagnostic performance was assessed by calibration/receiver operating characteristic curves and principal component analysis. A nine-lncRNAs signature (SLC12A5-AS1, LINC02028, KIZ-AS1, LINC02019, LINC01877, LINC01444, LINC01176, LINC01290, and LINC00581) was established in PTC, which has significant diagnostic and prognostic power. Functional enrichment analyses elucidated the regulatory mechanism of the nine-lncRNAs signature in the development of PTC. This signature and expressions of nine hub lncRNAs were correlated with the distributions of tumor infiltrating immune cells. A diagnostic nomogram was also established for PTC. By comparing with the published models with less than or equal to nine lncRNAs, our signature showed a preferable performace for prognosis prediction. In conclusion, our present research established an innovative nine-lncRNAs signature and a six-lncRNAs nomogram that might act as a potential indicator for PTC prognosis and diagnosis, which could be conducive to the PTC treatment.

Transcriptomics and metabolomics analyses provide insights into the difference in toxicity of benzo[a]pyrene and 6-chlorobenzo[a]pyrene to human hepatic cells

The toxicological information of chlorinated polycyclic aromatic hydrocarbons (Cl-PAHs), as derivatives of PAHs, is still relatively lacking. In this study, a combination of transcriptomics and metabolomics approach was adopted to explore the changes in toxicity to human L02 hepatocytes after chlorination of benzo[a]pyrene (B[a]P) at 6 position. In general, 6-Cl-B[a]P produced a stronger toxicity to human hepatic cells than did parent B[a]P. When exposure concentrations were 5 and 50 nM, 6-Cl-B[a]P caused a weaker transcriptomic perturbation relative to B[a]P, whereas a stronger metabolomic perturbation, a stronger oxidative stress and a stronger inhibition effect on cell viability were caused by 6-Cl-B[a]P than did parent B[a]P. Pathway enrichment analysis indicated that 6-Cl-B[a]P produced a more widely perturbation to metabolic pathways than did B[a]P. Although they both significantly impaired the function of mitochondrial electron transport chain (ETC), the exact mechanism is different. B[a]P suppressed the expression of 20 genes regulating mitochondrial ETC mainly via AhR activation. However, 6-Cl-B[a]P produced a stronger inhibition on the activities of complexes I and V than did B[a]P. Meanwhile, 6-Cl-B[a]P also exhibited a stronger inhibition effect on mitochondrial β oxidation of fatty acid. Furthermore, 6-Cl-B[a]P and B[a]P both significantly disturbed the nucleotide metabolism, glycerophospholipid metabolism and amino acid metabolism in L02 cells.