Lipoprotein (a) [Lp(a)] is a highly heterogeneous lipoprotein particle promoting panvascular disease. Structurally, it consists of an LDL-like core covalently bound to apolipoprotein (a) [apo(a)]. Mol Show more
Lipoprotein (a) [Lp(a)] is a highly heterogeneous lipoprotein particle promoting panvascular disease. Structurally, it consists of an LDL-like core covalently bound to apolipoprotein (a) [apo(a)]. Molecular determinants linking various genetic variants of apo(a) constituent of Lp(a) to vascular pathology remain incompletely defined. We have built a model allowing dissection which variations in LPA gene are functional, and which are mere associates of these functional variations. Copy number changes in kringle IV type 2 (KIV-2), together with a spectrum of single nucleotide polymorphisms (SNPs), regulate apo(a) size, expression, and function. These variants can be broadly categorized into Lp(a)-increasing, Lp(a)-lowering, and null alleles, with distinct prevalence across populations. Notably, risk alleles such as rs10455872 and rs3798220 account for substantial variance in circulating Lp(a) and confer elevated susceptibility to coronary artery disease, whereas splice-altering and nonsense alleles markedly reduce Lp(a) concentrations. The therapeutic implications of modifying circulating Lp(a) levels are profound. While conventional lipid-lowering therapies exert little influence on Lp(a), antisense oligonucleotides (pelacarsen) and small interfering RNA agents (olpasiran, SLN360) achieve robust Lp(a) reductions. Integrating genetic insights with structural modeling provides a framework to disentangle functional from proxy associations within LPA and neutralize the cardiovascular hazard conferred by elevated levels of Lp(a). Show less
Interactions of the extracellular matrix (ECM) and cellular receptors constitute one of the crucial pathways involved in colorectal cancer progression and metastasis. With the use of bioinformatics an Show more
Interactions of the extracellular matrix (ECM) and cellular receptors constitute one of the crucial pathways involved in colorectal cancer progression and metastasis. With the use of bioinformatics analysis, we comprehensively evaluated the prognostic information concentrated in the genes from this pathway. First, we constructed a ECM-receptor regulatory network by integrating the transcription factor (TF) and 5'-isomiR interaction databases with mRNA/miRNA-seq data from The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD). Notably, one-third of interactions mediated by 5'-isomiRs was represented by noncanonical isomiRs (isomiRs, whose 5'-end sequence did not match with the canonical miRBase version). Then, exhaustive search-based feature selection was used to fit prognostic signatures composed of nodes from the network for overall survival prediction. Two reliable prognostic signatures were identified and validated on the independent The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) cohort. The first signature was made up by six genes, directly involved in ECM-receptor interaction: AGRN, DAG1, FN1, ITGA5, THBS3, and TNC (concordance index 0.61, logrank test Show less