Reduced serum high-density lipoprotein cholesterol levels and aberrantly expressed cholesterol metabolism genes in colorectal cancer

Lipid Metabolism-Related Gene Signature Predicts Prognosis and Indicates Immune Microenvironment Infiltration in Advanced Gastric Cancer

Objective

Abnormal lipid metabolism is known to influence the malignant behavior of gastric cancer. However, the underlying mechanism remains elusive. In this study, we comprehensively analyzed the biological significance of genes involved in lipid metabolism in advanced gastric cancer (AGC).

Methods

We obtained gene expression profiles from The Cancer Genome Atlas (TCGA) database for early and advanced gastric cancer samples and performed differential expression analysis to identify specific lipid metabolism-related genes in AGC. We then used consensus cluster analysis to classify AGC patients into molecular subtypes based on lipid metabolism and constructed a diagnostic model using least absolute shrinkage and selection operator- (LASSO-) Cox regression analysis and Gene Set Enrichment Analysis (GSEA). We evaluated the discriminative ability and clinical significance of the model using the Kaplan-Meier (KM) curve, ROC curve, DCA curve, and nomogram. We also estimated immune levels based on immune microenvironment expression, immune checkpoints, and immune cell infiltration and obtained hub genes by weighted gene co-expression network analysis (WGCNA) of differential genes from the two molecular subtypes.

Results

We identified 6 lipid metabolism genes that were associated with the prognosis of AGC and used consistent clustering to classify AGC patients into two subgroups with significantly different overall survival and immune microenvironment. Our risk model successfully classified patients in the training and validation sets into high-risk and low-risk groups. The high-risk score predicted poor prognosis and indicated low degree of immune infiltration. Subgroup analysis showed that the risk model was an independent predictor of prognosis in AGC. Furthermore, our results indicated that most chemotherapeutic agents are more effective for AGC patients in the low-risk group than in the high-risk group, and risk scores for AGC are strongly correlated with drug sensitivity. Finally, we performed qRT-PCR experiments to verify the relevant results.

Conclusion

Our findings suggest that lipid metabolism-related genes play an important role in predicting the prognosis of AGC and regulating immune invasion. These results have important implications for the development of targeted therapies for AGC patients.

Targeting proprotein convertase subtilisin/kexin type 9 (PCSK9): from bench to bedside

Proprotein convertase subtilisin/kexin type 9 (PCSK9) has evolved as a pivotal enzyme in lipid metabolism and a revolutionary therapeutic target for hypercholesterolemia and its related cardiovascular diseases (CVD). This comprehensive review delineates the intricate roles and wide-ranging implications of PCSK9, extending beyond CVD to emphasize its significance in diverse physiological and pathological states, including liver diseases, infectious diseases, autoimmune disorders, and notably, cancer. Our exploration offers insights into the interaction between PCSK9 and low-density lipoprotein receptors (LDLRs), elucidating its substantial impact on cholesterol homeostasis and cardiovascular health. It also details the evolution of PCSK9-targeted therapies, translating foundational bench discoveries into bedside applications for optimized patient care. The advent and clinical approval of innovative PCSK9 inhibitory therapies (PCSK9-iTs), including three monoclonal antibodies (Evolocumab, Alirocumab, and Tafolecimab) and one small interfering RNA (siRNA, Inclisiran), have marked a significant breakthrough in cardiovascular medicine. These therapies have demonstrated unparalleled efficacy in mitigating hypercholesterolemia, reducing cardiovascular risks, and have showcased profound value in clinical applications, offering novel therapeutic avenues and a promising future in personalized medicine for cardiovascular disorders. Furthermore, emerging research, inclusive of our findings, unveils PCSK9's potential role as a pivotal indicator for cancer prognosis and its prospective application as a transformative target for cancer treatment. This review also highlights PCSK9's aberrant expression in various cancer forms, its association with cancer prognosis, and its crucial roles in carcinogenesis and cancer immunity. In conclusion, this synthesized review integrates existing knowledge and novel insights on PCSK9, providing a holistic perspective on its transformative impact in reshaping therapeutic paradigms across various disorders. It emphasizes the clinical value and effect of PCSK9-iT, underscoring its potential in advancing the landscape of biomedical research and its capabilities in heralding new eras in personalized medicine.

Mutant APC reshapes Wnt signaling plasma membrane nanodomains by altering cholesterol levels via oncogenic β-catenin

Although the role of the Wnt pathway in colon carcinogenesis has been described previously, it has been recently demonstrated that Wnt signaling originates from highly dynamic nano-assemblies at the plasma membrane. However, little is known regarding the role of oncogenic APC in reshaping Wnt nanodomains. This is noteworthy, because oncogenic APC does not act autonomously and requires activation of Wnt effectors upstream of APC to drive aberrant Wnt signaling. Here, we demonstrate the role of oncogenic APC in increasing plasma membrane free cholesterol and rigidity, thereby modulating Wnt signaling hubs. This results in an overactivation of Wnt signaling in the colon. Finally, using the Drosophila sterol auxotroph model, we demonstrate the unique ability of exogenous free cholesterol to disrupt plasma membrane homeostasis and drive Wnt signaling in a wildtype APC background. Collectively, these findings provide a link between oncogenic APC, loss of plasma membrane homeostasis and CRC development.

The oxidative aging model integrated various risk factors in type 2 diabetes mellitus at system level

Background

Type 2 diabetes mellitus (T2DM) is a chronic endocrine metabolic disease caused by insulin dysregulation. Studies have shown that aging-related oxidative stress (as "oxidative aging") play a critical role in the onset and progression of T2DM, by leading to an energy metabolism imbalance. However, the precise mechanisms through which oxidative aging lead to T2DM are yet to be fully comprehended. Thus, it is urgent to integrate the underlying mechanisms between oxidative aging and T2DM, where meaningful prediction models based on relative profiles are needed.

Methods

First, machine learning was used to build the aging model and disease model. Next, an integrated oxidative aging model was employed to identify crucial oxidative aging risk factors. Finally, a series of bioinformatic analyses (including network, enrichment, sensitivity, and pan-cancer analyses) were used to explore potential mechanisms underlying oxidative aging and T2DM.

Results

The study revealed a close relationship between oxidative aging and T2DM. Our results indicate that nutritional metabolism, inflammation response, mitochondrial function, and protein homeostasis are key factors involved in the interplay between oxidative aging and T2DM, even indicating key indices across different cancer types. Therefore, various risk factors in T2DM were integrated, and the theories of oxi-inflamm-aging and cellular senescence were also confirmed.

Conclusion

In sum, our study successfully integrated the underlying mechanisms linking oxidative aging and T2DM through a series of computational methodologies.