👤 Tia-Marje Korhonen

Lynch syndrome is a genetic cancer-predisposing syndrome caused by pathogenic mutations in DNA mismatch repair (path_MMR) genes. Due to the elevated cancer risk, novel screening methods, alongside current surveillance techniques, could enhance cancer risk stratification. Here we show how bi-omics integration could be utilized to pinpoint potential cancer-predicting biomarkers in Lynch syndrome. We studied which blood-based circulating microRNAs and metabolites could predict Lynch syndrome cancer occurrence within a 5.8-year prospective surveillance period. We used single- and bi-omics bioinformatic analyses and identified omics-level patterns and associations across these biological layers. Lasso Cox regression was used to highlight the most promising cancer-predicting biomarkers. Our findings revealed distinct circulating metabolite landscapes among path_MMR variant carriers and a circulating microRNA co-expression module significantly associated with future cancer incidence. These microRNAs regulate cancer-related pathways, including the PI3K/Akt signaling pathway. Additionally, a metabolite module consisting of ApoB-containing lipoproteins (low-, intermediate-, and very low-density lipoproteins) showed distinct levels across path_MMR variants. Notably, three biomarkers-hsa-miR-101-3p, hsa-miR-183-5p, and triglycerides in high-density lipoprotein particles (HDL_TG)-significantly predicted cancer risk, achieving a Harrel's Concordance Index (C-index) of 0.76 (p = .0007). Elevated levels of these biomarkers indicated increased cancer risk. Internal validation of the model yielded a C-index of 0.72. The bi-omics approach and the identified biomarkers offer promising insights for future studies regarding cancer risk identification in Lynch syndrome. Show less

Large-scale genome-wide studies of chronic hydrocephalus have been lacking. We conducted a genome-wide association study (GWAS) in normal pressure hydrocephalus (NPH). We used a case-control study design implementing FinnGen data containing 473,691 Finns with genotypes and nationwide health records. Patients with NPH were selected based on We included 1,522 patients with NPH (mean age 72.2 years, 53% women) and 451,091 controls (mean age 60.5 years, 44% women). In the GWAS comparing patients with NPH with the controls, we identified 6 gene regions significantly ( We identified 6 loci significantly associated with NPH in the thus far largest GWAS in chronic hydrocephalus. The genes near the top loci have previously been associated with blood-brain barrier and blood-CSF barrier function and with increased lateral brain ventricle volume. The effect sizes and allele frequencies remained similar in NPH and iNPH cohorts, indicating the identified loci are risk determinants for iNPH and likely not explained by associations with other etiologies. However, the exact role of these loci is still unknown, warranting further studies. Show less

Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity. Show less