The transcriptional risk scores for kidney renal clear cell carcinoma using XGBoost and multiple omics data.

Most kidney cancers are kidney renal clear cell carcinoma (KIRC) that is a main cause of cancer-related deaths. Polygenic risk score (PRS) is a weighted linear combination of phenotypic related alleles on the genome that can be used to assess KIRC risk. However, standalone SNP data as input to the PRS model may not provide satisfactory result. Therefore, Transcriptional risk scores (TRS) based on multi-omics data and machine learning models were proposed to assess the risk of KIRC. First, we collected four types of multi-omics data (DNA methylation, miRNA, mRNA and lncRNA) of KIRC patients from the TCGA database. Subsequently, a novel TRS method utilizing multiple omics data and XGBoost model was developed. Finally, we performed prevalence analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. Our TRS methods exhibited better predictive performance than the linear models and other machine learning models. Furthermore, the prediction accuracy of combined TRS model was higher than that of single-omics TRS model. The KM curves showed that TRS was a valid prognostic indicator for cancer staging. Our proposed method extended the current definition of TRS from standalone SNP data to multi-omics data and was superior to the linear models and other machine learning models, which may provide a useful implement for diagnostic and prognostic prediction of KIRC.

The construction of transcriptional risk scores for breast cancer based on lightGBM and multiple omics data.

BACKGROUND: Polygenic risk score (PRS) can evaluate the individual-level genetic risk of breast cancer. However, standalone single nucleotide polymorphisms (SNP) data used for PRS may not provide satisfactory prediction accuracy. Additionally, current PRS models based on linear regression have insufficient power to leverage non-linear effects from thousands of associated SNPs. Here, we proposed a transcriptional risk score (TRS) based on multiple omics data to estimate the risk of breast cancer. METHODS: The multiple omics data and clinical data of breast invasive carcinoma (BRCA) were collected from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO). First, we developed a novel TRS model for BRCA utilizing single omic data and LightGBM algorithm. Subsequently, we built a combination model of TRS derived from each omic data to further improve the prediction accuracy. Finally, we performed association analysis and prognosis prediction to evaluate the utility of the TRS generated by our method. RESULTS: The proposed TRS model achieved better predictive performance than the linear models and other ML methods in single omic dataset. An independent validation dataset also verified the effectiveness of our model. Moreover, the combination of the TRS can efficiently strengthen prediction accuracy. The analysis of prevalence and the associations of the TRS with phenotypes including case-control and cancer stage indicated that the risk of breast cancer increases with the increases of TRS. The survival analysis also suggested that TRS for the cancer stage is an effective prognostic metric of breast cancer patients. CONCLUSIONS: Our proposed TRS model expanded the current definition of PRS from standalone SNP data to multiple omics data and outperformed the linear models, which may provide a powerful tool for diagnostic and prognostic prediction of breast cancer.

Causal Associations of Anthropometric Measurements With Fracture Risk and Bone Mineral Density: A Mendelian Randomization Study.

Uncovering additional causal clinical traits and exposure variables is important when studying osteoporosis mechanisms and for the prevention of osteoporosis. Until recently, the causal relationship between anthropometric measurements and osteoporosis had not been fully revealed. In the present study, we utilized several state-of-the-art Mendelian randomization (MR) methods to investigate whether height, body mass index (BMI), waist-to-hip ratio (WHR), hip circumference (HC), and waist circumference (WC) are causally associated with two major characteristics of osteoporosis, bone mineral density (BMD) and fractures. Genomewide significant (p ≤ 5 × 10-8 ) single-nucleotide polymorphisms (SNPs) associated with the five anthropometric variables were obtained from previous large-scale genomewide association studies (GWAS) and were utilized as instrumental variables. Summary-level data of estimated bone mineral density (eBMD) and fractures were obtained from a large-scale UK Biobank GWAS. Of the MR methods utilized, the inverse-variance weighted method was the primary method used for analysis, and the weighted-median, MR-Egger, mode-based estimate, and MR pleiotropy residual sum and outlier methods were utilized for sensitivity analyses. The results of the present study indicated that each increase in height equal to a single standard deviation (SD) was associated with a 9.9% increase in risk of fracture (odds ratio [OR] = 1.099; 95% confidence interval [CI] 1.067-1.133; p = 8.793 × 10-10 ) and a 0.080 SD decrease of estimated bone mineral density (95% CI -0.106-(-0.054); p = 2.322 × 10-9 ). We also found that BMI was causally associated with eBMD (beta = 0.129, 95% CI 0.065-0.194; p = 8.113 × 10-5 ) but not associated with fracture. The WHR adjusted for BMI, HC adjusted for BMI, and WC adjusted for BMI were not found to be related to fracture occurrence or eBMD. In conclusion, the present study provided genetic evidence for certain causal relationships between anthropometric measurements and bone mineral density or fracture risk. © 2021 American Society for Bone and Mineral Research (ASBMR).