Serum Levels of the Cytokine TWEAK Are Associated with Metabolic Status in Patients with Prostate Cancer and Modulate Cancer Cell Lipid Metabolism In Vitro

Genome-wide association analysis identified inflammatory mechanisms mediating the effects of lipid metabolism on endometrial carcinoma in situ

BACKGROUND

While lipids and inflammation are recognized as key modulators of tumor progression, their causal interplay in endometrial carcinoma in situ (ECIS)-the precursor of endometrial cancer-remains mechanistically undefined. Despite broad associations between lipid metabolism and cancer, the specific pathways driving ECIS initiation through inflammatory mediators are unknown.

METHOD

We pioneer an integrative Mendelian randomization (MR) framework combining multivariable MR, Bayesian weighted MR (BWMR), and sensitivity analyses to address pleiotropy. This approach was systematically applied to 179 lipids and 91 inflammatory factors. Methodological novelty further includes mediation analysis quantifying inflammatory factors' role in lipid-ECIS pathways.

RESULTS

First evidence reveals a phosphatidylcholine (O-18:0_16:1)-TNFSF12-ECIS axis: TNFSF12 mediates 4.894 % of phosphatidylcholine's effect (OR: 2.925; beta: 1.073; 95 % CI: 1.752-4.884; p = 4.032E-05), attenuating the direct lipid-ECIS association. This represents the inaugural demonstration of an inflammation-mediated lipid pathway in ECIS pathogenesis.

CONCLUSION

As the first MR study decoding lipid-ECIS causality, we establish the following.

Genetically predicted TWEAK mediates the association between lipidome and Keratinocyte Carcinomas

BACKGROUND

Reports suggest that lipid profiles may be linked to the likelihood of developing skin cancer, yet the exact causal relationship is still unknown.

OBJECTIVE

This study aimed to examine the connection between lipidome and skin cancers, as well as investigate any possible mediators.

METHODS

A two-sample Mendelian randomization (MR) analysis was conducted on 179 lipidomes and each skin cancer based on a genome-wide association study (GWAS), including melanoma, basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). Then, Bayesian weighted MR was performed to verify the analysis results of two-sample MR. Moreover, a two-step MR was employed to investigate the impact of TNF-like weak inducer of apoptosis (TWEAK)-mediated lipidome on skin cancer rates.

RESULTS

MR analysis identified higher genetically predicted phosphatidylcholine (PC) (17:0_18:2) could reduce the risk of skin tumors, including BCC (OR = 0.9149, 95% CI: 0.8667-0.9658), SCC (OR = 0.9343, 95% CI: 0.9087-0.9606) and melanoma (OR = 0.9982, 95% CI: 0.9966-0.9997). The proportion of PC (17:0_18:2) predicted by TWEAK-mediated genetic prediction was 6.6 % in BCC and 7.6% in SCC. The causal relationship between PC (17:0_18:2) and melanoma was not mediated by TWEAK.

CONCLUSION

This study identified a negative causal relationship between PC (17:0_18:2) and keratinocyte carcinomas, a small part of which was mediated by TWEAK, and most of the remaining mediating factors are still unclear. Further research on other risk factors is needed in the future.

Multiplex protein analysis and ensemble machine learning methods of fine needle aspirates from prostate cancer patients reveal potential diagnostic signatures associated with tumour grade

BACKGROUND

Improved molecular diagnosis is needed in prostate cancer (PC). Fine needle aspiration (FNA) is a minimally invasive biopsy technique, less traumatic compared to core needle biopsy, and could be useful for diagnosis of PC. Molecular biomarkers (BMs) in FNA-samples can be assessed for prediction, eg of immunotherapy efficacy before treatment as well as at treatment decision time points during disease progression.

METHODS

In the present pilot study, the expression levels of 151 BM proteins were analysed by proximity extension assay in FNA-samples from 16 patients, including benign prostate lesions (n = 3) and cancers (n = 13). An ensemble data analysis strategy was applied using several machine learning models.

RESULTS

Twelve potentially predictive BM proteins correlating with International Society of Urological Pathology grade groups were identified, among them vimentin, tissue factor pathway inhibitor 2, and integrin beta-5. The validity of the results was supported by network analysis that showed functional associations between most of the identified putative BMs. We also showed that multiple immune checkpoint targets can be assessed (eg PD-L1, CD137, and Galectin-9), which may support the selection of immunotherapy in advanced PC. Results are promising but need further validation in a larger cohort.

CONCLUSIONS

Our pilot study represents a "proof of concept" and shows that multiplex profiling of potential diagnostic and predictive BM proteins is feasible on tumour material obtained by FNA sampling of prostate cancer. Moreover, our results demonstrate that an ensemble data analysis strategy may facilitate the identification of BM signatures in pilot studies when the patient cohort is limited.