p53-Mediated regulation of hepatic lipid metabolism: forging links between metabolism, atherogenesis, and cancer

Plasminogen missense variants and their involvement in cardiovascular and inflammatory disease

Human plasminogen (PLG), the zymogen of the fibrinolytic protease, plasmin, is a polymorphic protein with two widely distributed codominant alleles, PLG/Asp453 and PLG/Asn453. About 15 other missense or non-synonymous single nucleotide polymorphisms (nsSNPs) of PLG show major, yet different, relative abundances in world populations. Although the existence of these relatively abundant allelic variants is generally acknowledged, they are often overlooked or assumed to be non-pathogenic. In fact, at least half of those major variants are classified as having conflicting pathogenicity, and it is unclear if they contribute to different molecular phenotypes. From those, PLG/K19E and PLG/A601T are examples of two relatively abundant PLG variants that have been associated with PLG deficiencies (PD), but their pathogenic mechanisms are unclear. On the other hand, approximately 50 rare and ultra-rare PLG missense variants have been reported to cause PD as homozygous or compound heterozygous variants, often leading to a debilitating disease known as ligneous conjunctivitis. The true abundance of PD-associated nsSNPs is unknown since they can remain undetected in heterozygous carriers. However, PD variants may also contribute to other diseases. Recently, the ultra-rare autosomal dominant PLG/K311E has been found to be causative of hereditary angioedema (HAE) with normal C1 inhibitor. Two other rare pathogenic PLG missense variants, PLG/R153G and PLG/V709E, appear to affect platelet function and lead to HAE, respectively. Herein, PLG missense variants that are abundant and/or clinically relevant due to association with disease are examined along with their world distribution. Proposed molecular mechanisms are discussed when known or can be reasonably assumed.

Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis

Background

The early-stage lung adenocarcinoma (LUAD) rate has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure.

Methods

LMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response.

Results

First, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results.

Conclusions

This is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.

Lipid metabolism gene-wide profile and survival signature of lung adenocarcinoma

Background

Lung cancer has high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. Disordered lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on the diagnosis and prognostic biomarkers of LUAD.

Methods

In this study, expression analysis of 1045 lipid metabolism-related genes was performed between LUAD tumors and normal tissues derived from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differentially expressed genes (DEGs) was constructed to identify the hub genes. The association between hub genes and overall survival (OS) was evaluated and formed a model to predict the prognosis of LUAD using a nomogram. The model was validated by another cohort, GSE13213.

Results

A total of 217 lipid metabolism-related DEGs were detected in LUAD. Genes were significantly enriched in glycerophospholipid metabolism, fatty acid metabolic process, and eicosanoid signaling. Through network analysis and cytoHubba, 6 hub genes were identified, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. High expression of CYP2C9, UGT1A6, and INS, and low expressions of DGAT1, HPGDS, and LPL, were associated with worse overall survival for 1925 LUAD patients. The model showed that the high-risk score group had a worse OS, and the validated cohort showed the same result.

Conclusions

In this study, a signature of 6 lipid metabolism genes was constructed, which was significantly associated with the diagnosis and prognosis of LUAD patients. Thus, the gene signature can be used as a biomarker for LUAD.