Integrative analysis of methylation and transcriptional profiles to - - PowerPoint PPT Presentation

Integrative analysis of methylation and transcriptional profiles to predict aging and construct aging-specific cross- tissue network

Yin Wang Fudan University

Outline

• Introduction
• TCGA data
• Integrating and Stepwise Age-Prediction

pipeline

• PPI network
• Functional / enrichment analysis
• Aging cross-tissue network
• Aging pathway interaction network
• Conclusion

Introduction

• Aging is emerging as an interesting topic,

as aging has been shown to be involved in many disorders, such as Parkinson disease, diabetes and cancers.

• Profiling patterns of crucial DNA

methylation / mRNA markers change with the chronological age

• Many predictors have been applied to

identify aging biomarkers and analyze aging functions

Our previous study

• mRMR method and kNN classifier

Aging cross-talk networks

Introduction

Integrative aging networks from multi-scale data (i.e. methylation and expression) have not been constructed entirely in Homo species

• Reconstructing molecular networks also

gives systematic approaches to deal with multi-scale data in aging analysis

TCGA data and pre-processing

• input: methylation and expression; output:

clinical data

• training data：BLCA，BRCA，HNSC，

KIRC，LUAD，THCA；216 samples

• test data：KIRP，LIHC，PRAD； 99

samples

• svd method was used to assess the

sources of inter-sample variation

• z-score method

Integrating and Stepwise Age- Prediction method

• Lasso regression for

methylation data with age

• Determine λ value by

cross-validation

• Regress residuals by

gene expression profiles

• sort abs(corr)
• Determine genes number

(PLS)

• Determine PLS vectors
• 6-fold and 5-fold

Prediction results

Prediction results

Prediction results

PPI network

• Data from String database
• confidence score >700（0.7）
• Dijkstra algorithm to find shortest path
• Controlled by permutation test

gene between ness p-value TP53 1023 0.016* HSP90A A1 665 0.009* SRC 363 0.086 STA T3 263 0* BMP2 254 0* AKT1 243 0.759 CD8A 235 0* EP300 229 0* HSPA4 221 0* IL6 207 0.018*

functional / enrichment analysis

• methylation of GPR45
• expression of CORO6 in kidney
• positive regulation of immune system process

(GO:0007059, p-value=1.6643e-08, and FDR =1.3308e-05) and cell adhesion molecules (CAMs, p-value=1.4205e-06, and FDR =2.4517e-04)

• negative regulation of phosphate metabolic

process (GO:0045936, p-value=9.7695e-05, and FDR =0.0157) in kidney renal papillary cell and Antigen processing and presentation pathway (p- value=3.3052e-06, and FDR =0.0006) in thyroid

Cross-tissue network

• young age: ≤50; old age: ≥60 (more than 3

samples), 7 tissues and 21 tissut-tissue pairs

• profiles were discretized using two thresholds

mean+/-std

• Kolmogorov-Smirnov (K-S) value of cumulative

distribution between different tissues

• absolute difference of K-S value between old

and young group was set as the edge (>0.95)

Cross-tissue network

• 31 pairs in 6 tissue-

tissue

• GATA4→EGFL7

shares 4 GO terms (development process)

• positive regulation of

caspase activity (GO:0043280, p- value=9.8594e-05, and FDR =0.0717)

pathway interaction network

• KEGG pathway p-value<0.05 and

FDR<0.25

• 7 tissues
• summarizing K-S values between

pathways from different tissues

pathway interaction network

• sum of absolute K-S value

differences (>0.6)

sum of all absolute K-S value differences

FDR<0.1

Conclusion

• Cellular senescence control aging in

immunosenescence theories

• Cell adhesion cascades, cell cycle and

neurotrophin pathway played important roles in the aging process altogether

• head / neck and kidney

Summary

• The pipeline find key aging markers with

high accuracy

• Network analysis (PPI, cross-tissue and

pathway interaction) revealed coordinated aging patterns

Acknowledgement

• Prof. Yixue Li, Lei Liu, and Lu Xie
• Associate Prof. Tao Huang
• Thank you !