Omics A Journal of Integrative Biology最新文献

A systems medicine understanding of the regulatory molecular circuits that underpin breast cancer is essential for early cancer detection and precision/personalized medicine in clinical oncology. Transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) control gene expression and cell biology, and by extension, serve as pillars of the regulatory circuits that determine human health and disease. We report here the development of a regulatory circuit analysis program, miRCuit, constructing 10 different types of regulatory elements involving messenger RNA, miRNA, lncRNA, and TFs. Using the miRCuit, we analyzed expression profiling data from 179 invasive ductal breast carcinoma and 51 normal tissue samples from the Gene Expression Omnibus database. We identified eight circuit types along with two special types of circuits, one of which highlighted the significant roles of lncRNA CASC15, miR-130b-3p, and TF KLF5 in breast cancer development and progression. These findings advance our understanding of the regulatory molecules associated with breast cancer. Moreover, miRCuit offers a new avenue for users to construct circuits from regulatory molecules for potential applications to decipher disease pathogenesis.

UN Women is the United Nations "entity dedicated to gender equality and the empowerment of women". UN Women is an example of the institutions of global governance that followed the gender turn in women's rights over the past 2 decades. This opinion commentary unpacks a brief history of UN Women, and the ongoing disparities in gender diversity, equity, and inclusion (DEI) in science, engineering, and medicine, not to mention in science communication, with the aim to shed light on the adverse impacts of gender essentialism and gender binary. First, I argue that another world and liberatory structural change are indeed possible by resisting and refusing empty platitudes for band-aid solutions, disingenuous pleasantries and cultures of scheming for professional ladder-climbing that cloak the systemic causes-of-causes and sustain DEI inequities. Second, I argue for systems thinking and reflexive change in research cultures through queering global science, and rethinking everyday hegemonic assumptions and the prevailing blind spots in sex, gender, science, and society. Third, queer theory is not limited to studies of gender and sexuality. When used as a verb, "queering," its meaning broadens so as to mean critical examination of the unchecked assumptions and norms in a given field of scholarly inquiry. The DEI inequities in science, engineering, and medicine are real, harmful to individuals and communities in the present historical moment, and undermine intergenerational justice, not to mention hinder science and innovation. Going forward in the current decade amid uncertainty and polycrisis in world affairs and global democracy, the systemic gaps in gender equity in everyday laboratory life and on the streets ought to be remedied for global science and planetary health to be just, responsible, democratic, and innovative.

Structural variation (SV) typically refers to alterations in DNA fragments at least 50 base pairs long in the human genome. It can alter thousands of DNA nucleotides and thus significantly influence human health, disease, and clinical phenotypes. There is a shared and growing recognition that the emergence of effective computational tools and high-throughput technologies such as short-read sequencing and long-read sequencing offers novel insight into SV and, by extension, diseases affecting planetary health. However, numerous available SV tools exist with varying strengths and weaknesses. This is currently hampering the abilities of scholars to select the optimal tools to study SVs. Here, we reviewed 175 tools developed in the past two decades for SV detection, annotation, visualization, and downstream analysis of human genomics. In this expert review, we provide a comprehensive catalog of SV-related tools across different technology platforms and summarize their features, strengths, and limitations with an eye to accelerate systems science and planetary health innovations.

The current decade 2021-2030 was designated by the United Nations as the decade of healthy aging, which underlines the need for public health innovation for arthritis clinical care. The triglyceride-glucose (TyG) index is a novel and emerging parameter closely associated with diabetes and cardiovascular diseases and has been suggested to indicate the risk of arthritis. This study examined the longitudinal changes of TyG levels in relation to arthritis among a nationwide cohort of older Chinese adults. We recruited 1257 participants from a national cohort of older Chinese adults, the Chinese Longitudinal Healthy Longevity Survey. On the basis of the longitudinal changes in TyG between 2012 and 2014, we performed a k-means clustering analysis to classify the participants into four TyG groups: Class 1 with moderate and stable levels of TyG; Class 2 with low but rising level of TyG; Class 3 with consistently high TyG; and Class 4 with high and TyG-level rise compared with the baseline. After a 2-year follow-up, logistic regression was used to identify the association between TyG and the onset of arthritis. Compared with individuals in Class 1, those in Class 3 and Class 4 experienced a higher risk of arthritis, with an odds ratio (OR) of 2.823 (95% confidence interval [CI]: 1.113-7.160) and 2.848 (95% CI: 1.299-6.246), respectively. To the best of our knowledge, this is the first study exploring the association between dynamic longitudinal changes in TyG and arthritis. Further studies on world populations are called for.

The journey of a biomarker from analytical and clinical validation to clinical and public health utility is laden with a host of challenges. This opinion piece and innovation analysis presents an approach to biomarker discovery and development with a focus on tissue specificity and disease causality, using the case of hepatic disease.

Next-generation cancer phenomics by deployment of multiple molecular endophenotypes coupled with high-throughput analyses of gene expression offer veritable opportunities for triangulation of discovery findings in non-small cell lung cancer (NSCLC) research. This study reports differentially expressed genes in NSCLC using publicly available datasets (GSE18842 and GSE229253), uncovering 130 common genes that may potentially represent crucial molecular signatures of NSCLC. Additionally, network analyses by GeneMANIA and STRING revealed significant coexpression and interaction patterns among these genes, with four notable hub genes-GRK5, CAV1, PPARG, and CXCR2-identified as pivotal in NSCLC progression. Validation of these hub genes indicated their consistent downregulation in tumor tissues compared to normal counterparts. Gene expression across the endophenotypes representing pathological stages revealed distinct downregulation trends, emphasizing their putative roles as biomarkers for cancer progression. Moreover, three miRNAs (hsa-miR-429, hsa-miR-335-5p, and hsa-miR-126-3p) showed strong associations with these hub genes, while SREBF1 emerged as a relevant transcription factor. Pathway enrichment analysis identified the chemokine signaling pathway as significantly associated with these genes, highlighting its role in tumor progression and immune evasion. Cell-type enrichment analysis indicated that endothelial cells may play a significant role in NSCLC pathogenesis. Finally, survival analysis demonstrated that GRK5 is a potential oncogenic marker, whereas CAV1 may have a protective effect. These findings collectively underscore the critical molecular interactions in NSCLC and suggest novel paths for translational research, targeted therapies, and prognostic markers in clinical settings. They also attest to the promises of next-generation cancer phenomics using multiple endophenotypes for discovery and triangulation of novel findings.

Artificial intelligence (AI) and its applications in digital health, bioengineering, and society have significant material impacts on the environment owing to AI's vast energy demands and energy consumption, carbon footprints, and water usage to cool data centers and generate electricity to power the data centers. Yet, the environmental footprints of AI remain underappreciated and inadequately acknowledged. This is significant, particularly in this era of climate emergency and ongoing threats to planetary energy and water supplies. The vocabulary attached to AI often aims to mimic positive human capacities such as "warmness" and "care." However, these attempts to humanize AI and digital technology come with an anthropocentric gaze and blind spots that bracket out the environmental impacts and footprints of AI and privilege humans and technology over nonhuman animals and planetary ecological limits. In medicine, the environmental impacts of large language models range from water consumption and carbon emission to rare mineral usage. This commentary and innovation analysis question and queer the popular imagination of AI and digital technology as things that only exist in the immaterial world of cyberspace. In the course of research on AI in planetary health, we must be cognizant of its materiality, ecological impacts, and massive energy and water demands. We argue that moving away from anthropocentric narratives and vocabulary in AI design and praxis would bode well to live within planetary ecological limits so that AI and emerging digital technologies best serve robust and responsible science and all life on the planet Earth.

Gastrointestinal cancers pose a significant global health challenge. N-glycosylation modulates various cellular processes, including key cancer-related mechanisms. Elucidating its involvement in the onset and advancement of these cancers can offer critical insights for enhancing diagnostic and therapeutic approaches. This review outlines the core process of protein N-glycosylation and highlights its contribution to the progression of gastrointestinal cancers, encompassing cell proliferation, survival, invasion, metastasis, and immune evasion, mainly through its impact on critical signaling pathways. Notably, aberrant N-glycosylation patterns have emerged as crucial biomarkers for the diagnosis and prognosis of various gastrointestinal cancers, providing the foundation for more personalized therapeutic approaches. Therapeutic strategies targeting N-glycosylation, such as glycosyltransferase inhibitors and glycoengineering, show significant promise in mitigating tumor aggressiveness and enhancing immune recognition. However, the clinical implementation of N-glycosylation biomarkers requires the standardization of glycosylation analysis techniques and solutions to challenges in sample processing and data interpretation. Future research efforts should concentrate on overcoming these obstacles to unlock the full potential of N-glycosylation in enhancing cancer management and advancing patient outcomes.

Age-related hearing impairment (ARHI) is a major planetary health burden that is in need of precision medicine for prevention, diagnosis, and treatment. The present study was set out to identify candidate epigenetic markers for ARHI. Associations of genetically predicted DNA methylation levels with ARHI risk were evaluated using two sets of blood DNA methylation genetic prediction models in 147,997 cases and 575,269 controls of European descent. A total of 1314 CpG sites (CpGs) were significantly associated with ARHI risk at a false discovery rate (FDR) <0.05, including 12 putatively causal CpGs based on fine-mapping analysis. Measured methylation levels of 247 of the associated CpGs were significantly correlated with measured expression levels of 127 nearby genes in blood at an FDR <0.05. A total of 37 CpGs and their 18 nearby genes showed consistent association directions for the methylation-gene expression-ARHI risk pathway. Importantly, three genes (PEX6, TCF19, and SPTBN1) were enriched in auditory disease categories. Our results indicate that specific CpGs may modulate ARHI risk by regulating the expression of candidate ARHI target genes. Future precision medicine and biomarker development research on ARHI are called for.

Lung cancer remains one of the leading causes of cancer-related deaths globally, with its complexity driven by intricate and intertwined genetic, epigenetic, and environmental factors. Despite advances in genomics, transcriptomics, and proteomics, understanding the phenotypic diversity of lung cancer has lagged behind. Next-generation phenomics, which integrates high-throughput phenotypic data with multiomics approaches and digital technologies such as artificial intelligence (AI), offers a transformative strategy for unraveling the complexity of lung cancer. This approach leverages advanced imaging, single-cell technologies, and AI to capture dynamic phenotypic variations at cellular, tissue, and whole organism levels and in ways resolved in temporal and spatial contexts. By mapping the high-throughput and spatially and temporally resolved phenotypic profiles onto molecular alterations, next-generation phenomics provides deeper insights into the tumor microenvironment, cancer heterogeneity, and drug efficacy, safety, and resistance mechanisms. Furthermore, integrating phenotypic data with genomic and proteomic networks allows for the identification of novel biomarkers and therapeutic targets in ways informed by biological structure and function, fostering precision medicine in lung cancer treatment. This expert review examines and places into context the current advances in next-generation phenomics and its potential to redefine lung cancer diagnosis, prognosis, and therapy. It highlights the emerging role of AI and machine learning in analyzing complex phenotypic datasets, enabling personalized therapeutic interventions. Ultimately, next-generation phenomics holds the promise of bridging the gap between molecular alterations and clinical and population health outcomes, providing a holistic understanding of lung cancer biology that could revolutionize its management and improve patient survival rates.