IRF family genes have been shown to be crucial in tumorigenesis and tumour immunity. However, information about the role of IRF in the systematic assessment of pan‐cancer and in predicting the efficacy of tumour therapy is still unknown. In this work, we performed a systematic analysis of IRF family genes in 33 tumour samples, including expression profiles, genomics and clinical characteristics. We then applied Single‐Sample Gene‐Set Enrichment Analysis (ssGSEA) to calculate IRF‐scores and analysed the impact of IRF‐scores on tumour progression, immune infiltration and treatment efficacy. Our results showed that genomic alterations, including SNPs, CNVs and DNA methylation, can lead to dysregulation of IRFs expression in tumours and participate in regulating multiple tumorigenesis. IRF‐score expression differed significantly between 12 normal and tumour samples and the impact on tumour prognosis and immune infiltration depended on tumour type. IRF expression was correlated to drug sensitivity and to the expression of immune checkpoints and immune cell infiltration, suggesting that dysregulation of IRF family expression may be a critical factor affecting tumour drug response. Our study comprehensively characterizes the genomic and clinical profile of IRFs in pan‐cancer and highlights their reliability and potential value as predictive markers of oncology drug efficacy. This may provide new ideas for future personalized oncology treatment.
Often, biomedical researchers take a tissue sample from a tumour that unintentionally contains tumour cells and stromal cells so that gene expression from the sample represents two distinct kinds of cells. This is tolerated because techniques for separating tumour and stromal cells are expensive and time‐consuming and the act of separation can alter gene expression. So, it is desirable to have a technique for estimating the proportion of tumour cells in a mixed sample to improve detection of differential expression in cancer cells. © 2014 The Authors. Stat Published by John Wiley & Sons Ltd.
more » « less- NSF-PAR ID:
- 10197273
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Stat
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2049-1573
- Page Range / eLocation ID:
- p. 313-325
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Motivation Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample.
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Availability and implementation R codes can be found at https://github.com/petraf01/TSNet.
Supplementary information Supplementary data are available at Bioinformatics online.
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