Integrative gene set analysis of multi-platform data with sample heterogeneity

Identification and verification of prognostic cancer subtype based on multi-omics analysis for kidney renal papillary cell carcinoma

Background

Identifying Kidney Renal Papillary Cell Carcinoma (KIRP) patients with high-risk, guiding individualized diagnosis and treatment of patients, and identifying effective prognostic targets are urgent problems to be solved in current research on KIRP.

Methods

In this study, data of multi omics for patients with KIRP were collected from TCGA database, including mRNAs, lncRNAs, miRNAs, data of methylation, and data of gene mutations. Data of multi-omics related to prognosis of patients with KIRP were selected for each omics level. Further, multi omics data related to prognosis were integrated into cluster analysis based on ten clustering algorithms using MOVICS package. The multi omics-based cancer subtype (MOCS) were compared on biological characteristics, immune microenvironmental cell abundance, immune checkpoint, genomic mutation, drug sensitivity using R packages, including GSVA, clusterProfiler, TIMER, CIBERSORT, CIBERSORT-ABS, quanTIseq, MCPcounter, xCell, EPIC, GISTIC, and pRRophetic algorithms.

Results

The top ten OS-related factors for KIRP patients were annotated. Patients with KIRP were divided into MOCS1, MOCS2, and MOCS3. Patients in the MOCS3 subtype were observed with shorter overall survival time than patients in the MOCS1 and MOCS2 subtypes. MOCS1 was negatively correlated with immune-related pathways, and we found global dysfunction of cancer-related pathways among the three MOCS subtypes. We evaluated the activity profiles of regulons among the three MOCSs. Most of the metabolism-related pathways were activated in MOCS2. Several immune microenvironmental cells were highly infiltrated in specific MOCS subtype. MOCS3 showed a significantly lower tumor mutation burden. The CNV occurrence frequency was higher in MOCS1. As for treatment, we found that these MOCSs were sensitive to different drugs and treatments. We also analyzed single-cell data for KIRP.

Conclusion

Based on a variety of algorithms, this study determined the risk classifier based on multi-omics data, which could guide the risk stratification and medication selection of patients with KIRP.

Candidate Gene of NOS3, MMP3, AGT, and AGT1R and Pathway Analyses for Platelet Reactivity and Clinical Outcomes of Repeat Revascularization After First PCI in Chinese Patients

PURPOSE

Major disadvantages of the percutaneous coronary intervention (PCI) are the high occurrence of repeat revascularization due to restenosis and disease progression. The current study aimed to identify indicators that can predict the risk of repeat revascularization.

METHODS

A total of 143 patients who underwent PCI and had genetic test results were enrolled. We retrospectively reviewed their medical records after the first PCI. P2Y12 reaction unit (PRU) test results were obtained by VerifyNow; 4 candidate genes (NOS3, MMP3, AGT, and AGT1R) and 380 genes related to platelet activation-related processes and clopidogrel activity were selected for analysis. Repeat revascularization and in-stent restenosis (ISR) were used as clinical outcomes, and PRU and ADP aggregation rates were used as platelet function outcomes in analysis.

RESULTS

After the first PCI, the incidence of repeat revascularization at 18, 30, and 42 months was 14.1% (20/142), 17.5% (24/137), and 39.7% (31/78), respectively. In the candidate gene analysis, rs7830 (NOS3) was associated with both ADP aggregation rate and 18- and 30-month ISR, and rs 62,275,847 (AGTR1) was associated with both ADP aggregation rate and 30-month ISR. In the pathway, gene-set analysis, the linkage rs471683 and rs7785386 of GNAI1|GNAT3 were associated with PRU and ADP aggregation rate, 18-month and 30-month ISR, and repeat revascularization within 30 months. Rs1715389 of GNAI1|GNAT3 was associated with both PRU and ADP aggregation rate, 18-month and 30-month ISR, and repeat revascularization within 30 months. Rs7313458 of ITPR2 was associated with PRU and ADP aggregation rate, 18-month and 30-month ISR, and repeat revascularization within 18 months.

CONCLUSIONS

The genetic polymorphisms of rs7830 (NOS3), rs62275874 (AGTR1), linkage rs471683 and rs7785386 (GNAI1|GNAT3), rs1715389 (GNAI1|GNAT3), and rs7313458 (ITPR2) may lead to an increased risk of in-stent restenosis and revascularization after the first PCI in Chinese patients by affecting the efficacy of clopidogrel. The above six SNP may be used as potential genetic biomarkers for high risk of in-stent restenosis and revascularization after the first PCI in Chinese patients.

Gene expression analysis of combined RNA-seq experiments using a receiver operating characteristic calibrated procedure

Because of rapid advancements in sequencing technology, the experimental platforms of RNA-seq are updated frequently. It is quite common to combine data sets from several experimental platforms for analysis in order to increase the sample size and achieve more powerful tests for detecting the presence of differential gene expression. The data sets combined from different experimental platforms will have a complex data distribution, which causes a major problem in statistical modeling as well as in multiple testing. Although plenty of research have studied this problem by modeling the batch effects, there are no general and robust data-driven procedures for RNA-seq analysis. In this paper we propose a new robust procedure which combines the use of popular methods (packages) with a data-driven simulation (DDS). We construct the average receiver operating characteristic curve through the DDS to provide the calibrated levels of significance for multiple testing. Instead of further modifying the adjusted p-values, we calibrated the levels of significance for each specific method and mean effect model. The procedure was demonstrated with several popular RNA-seq analysis methods (edgeR, DEseq2, limma+voom). The proposed procedure relaxes the stringent assumptions of data distributions for RNA-seq analysis methods and is illustrated using colorectal cancer studies from The Cancer Genome Atlas database.

Gene-Set Integrative Analysis of Multi-Omics Data Using Tensor-based Association Test

MOTIVATION

Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference.

RESULTS

We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis.

AVAILABILITY AND IMPLEMENTATION

R function and instruction are available from the authors' website: https://www4.stat.ncsu.edu/∼jytzeng/Software/TR.omics/TRinstruction.pdf.

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

Candidate gene and pathway analyses identifying genetic variations associated with prasugrel pharmacokinetics and pharmacodynamics

AIM

We aimed to investigate the genetic polymorphisms and pharmacogenetic variability associated with the pharmacodynamics (PD) and pharmacokinetics (PK) of prasugrel, in healthy Han Chinese subjects.

PATIENTS & METHODS

Healthy, native, Han Chinese subjects (n = 36) aged 18 to 45 years with unknown genotypes were included. All subjects received a loading dose (LD) on day 1 and a maintenance dose (MD) from day 2 until day 11. Candidate gene association and gene-set analysis of biological pathways related to prasugrel and platelet activity were analyzed.

RESULTS

28 SNPs of 17 candidate genes previously associated with prasugrel or platelet activity were selected after a literature search. In the 30 mg LD groups (n = 24), ITGA2-rs28095 was found to be significantly associated with the P2Y12 reaction unit (PRU) value at 24 h after the LD (p = 0.015). 165 study genes related to platelet activation-related processes and prasugrel activity were selected from the MSigDB database, including curated gene sets from KEGG, Bio Carta, and Gene Cards. 14 SNPs of 9 genes were found to be significantly correlated both at 24 h and 12 days after LD: ADAMTSL1, PRKCA, ITPR2, P2RY12, P2RY14, PLCB4, PRKG1, ADCY1, and LYN. Seven SNPs of 6 protein-coding genes associated with area under the concentration-time curve (AUC0-tlast) were significantly identified among the 47 selected genes, including ADAMTSL1, CD36, P2RY1, PCSK9, PON1, and SCD.

CONCLUSION

These results show that genetic variation affects the PK and PD of prasugrel in normal individuals. Further studies with larger sample sizes are required to explore whether the SNPs are associated only with prasugrel activity or also with cardiovascular events and all-cause mortality.

Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization

With the rapid advances in technologies of microarray and massively parallel sequencing, data of multiple omics sources from a large patient cohort are now frequently seen in many consortium studies. Effective multi-level omics data integration has brought new statistical challenges. One important biological objective of such integrative analysis is to cluster patients in order to identify clinically relevant disease subtypes, which will form basis for tailored treatment and personalized medicine. Several methods have been proposed in the literature for this purpose, including the popular iCluster method used in many cancer applications. When clustering high-dimensional omics data, effective feature selection is critical for better clustering accuracy and biological interpretation. It is also common that a portion of "scattered samples" has patterns distinct from all major clusters and should not be assigned into any cluster as they may represent a rare disease subcategory or be in transition between disease subtypes. In this paper, we firstly propose to improve feature selection of the iCluster factor model by an overlapping sparse group lasso penalty on the omics features using prior knowledge of inter-omics regulatory flows. We then perform regularization over samples to allow clustering with scattered samples and generate tight clusters. The proposed group structured tight iCluster method will be evaluated by two real breast cancer examples and simulations to demonstrate its improved clustering accuracy, biological interpretation, and ability to generate coherent tight clusters.