Genetic association and single-cell transcriptome analyses reveal distinct features connecting autoimmunity with cancers

Two-sample Mendelian Randomization to evaluate the causal relationship between inflammatory arthritis and female-specific cancers

BACKGROUND

There is evidence that inflammatory arthritis in the form of ankylosing spondylitis (AS), psoriatic arthritis (PsA), and rheumatoid arthritis are both positively and negatively associated with certain female-specific cancers. However, the study results are very heterogeneous.

METHODS

Based on up to 375,814 European women, we performed an iterative two-sample Mendelian randomization to assess causal effects of the occurrence of the inflammatory arthritis on the risk of female-specific cancer in form of breast, endometrial, and ovarian cancer sites as well as their subtypes. Evidence was strengthened by using similar exposures for plausibility or by replication with a subsequent meta-analysis. P-values were Bonferroni adjusted.

RESULTS

Genetic liability to AS was associated with ovarian cancer (OR = 1.03; 95% CI: [1.01; 1.04]; [Formula: see text]=0.029) and liability to PsA with breast cancer (OR = 1.02; CI: [1.01; 1.04]; [Formula: see text]=0.002). Subgroup analyses revealed that the high-grade serous ovarian cancer (OR = 1.04; CI: [1.02; 1.06]; [Formula: see text]=0.015) and the ER- breast cancer (OR = 1.04; CI: [1.01; 1.07]; [Formula: see text]=0.118) appeared to drive the observed associations, respectively. No further associations were found between the remaining inflammatory arthritis phenotypes and female-specific cancers.

CONCLUSIONS

This study suggests that AS is a risk factor for ovarian cancer, while PsA is linked to an increased breast cancer risk. These results are important for physicians caring women with inflammatory arthritis to advise their patients on cancer screening and preventive measures.

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.