Search PubMedSearch

PubMed · 39955316

Association of cancers with the occurrence and 28-day mortality of sepsis: a mendelian randomization and mediator analysis.

Abstract

Observational studies have indicated an association between cancer and the occurrence of sepsis, with an increased risk of mortality in cancer-related sepsis. However, whether a causal relationship exists between the two remains unknown. Summary statistics of thirteen cancers from the largest available genome-wide association studies (GWAS) of GWAS catalog and FinnGen biobank were extracted for the MR analysis. GWAS data for sepsis and its 28-day mortality were obtained from MRC-IEU. Univariable, multivariable, and reverse MR analyses were employed to explore potential associations between cancers and sepsis and its 28-day mortality. Moreover, a two-step mediation MR analysis was performed to investigate independent positive causal relationships between cancers and sepsis and its 28-day mortality. In univariable Mendelian randomization (MR) analysis, significant causal relationships were found between genetically predicted lung cancer (OR = 1.17, 95% CI = 1.08-1.26, adjusted p = 0.001), squamous cell lung carcinoma (OR = 1.10, 95% CI = 1.02-1.18, adjusted p = 0.042), lung adenocarcinoma (OR = 1.12, 95% CI = 1.03-1.21, adjusted p = 0.032), small cell lung carcinoma (OR = 1.07, 95% CI = 1.02-1.12, adjusted p = 0.031), and sepsis. Subsequent multivariable MR analysis revealed that these three types of lung cancer were independently associated with the risk of sepsis. Additionally, a causal relationship was found between lung cancer and 28-day mortality from sepsis, while no causal link was observed between non-solid tumors and the onset or death of sepsis. Reverse MR analysis did not indicate a potential for sepsis to trigger the onset of cancers. Furthermore, TRAIL was found to have promotive effects on the occurrence and mortality of sepsis. Lung cancer causally correlates with increased sepsis occurrence and 28-day mortality, as evidenced by Mendelian Randomization analysis. Genetic predispositions enhance this risk, underscoring the potential of genetic profiling to guide early, precise sepsis interventions in these patients.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dengwei Cheng, Shangwen Pan, Xiangzhi Fang, Su Wang, Xiaojing Zou, Huaqing Shu, Xiaobo Yang, Jiqian Xu, You Shang. 2025-02-15. Association of cancers with the occurrence and 28-day mortality of sepsis: a mendelian randomization and mediator analysis.. https://doi.org/10.1038/s41598-025-89354-w

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans