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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Pathway-Based Personalized Analysis of Pan-Cancer Transcriptomic Data.

Cong Pian¹, Mengyuan He¹, Yuanyuan Chen¹

¹Department of Mathematics, College of Science, Nanjing Agricultural University, Nanjing 210095, China.

|November 27, 2021

Summary

Cancer pathway deregulation is linked to malignancy. Analyzing transcriptomic data identified cancer-specific and prognostic pathways, revealing potential therapeutic targets for improved cancer treatment.

Keywords:

cancer-specific deregulated pathways pan-cancer pathway deregulation scores prognostic model prognostic pathways

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Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Cancer development is associated with the dysregulation of specific molecular pathways.
Pathway deregulation scores (PDS) offer a method to quantify pathway activity from transcriptomic data.

Purpose of the Study:

To identify cancer-specific and prognostic pathways across 13 cancer types using transcriptomic data.
To investigate the relationship between pathway deregulation and cancer malignancy and prognosis.
To develop and validate a prognostic model for cancer survival prediction.

Main Methods:

Utilized the Pathifier algorithm to calculate PDS from The Cancer Genome Atlas (TCGA) transcriptomic data.
Analyzed data from 13 distinct cancer types to identify deregulated and prognostic pathways.
Constructed a Cox proportional Hazards model using identified prognostic pathways and validated it with the METABRIC database.

Main Results:

Individual PDS effectively distinguished between different cancer types and their adjacent tissues.
Significant heterogeneity was observed in cancer-specific and prognostic pathways across cancer types.
Cancers with a higher number of deregulated pathways exhibited increased malignancy and poorer prognoses.
The Cox model accurately predicted survival and prognosis, with the breast cancer model validated independently.

Conclusions:

Identified prognostic pathways hold potential as therapeutic targets for cancer treatment.
Pathway deregulation analysis provides insights into cancer biology and patient outcomes.
The developed prognostic model demonstrates clinical utility for predicting cancer survival.