Changes in gene-gene interactions associated with cancer onset and progression are largely independent of changes in gene expression

Abstract

Summary Recent findings indicate that changes underlying cancer onset and progression are not only attributable to changes in DNA structure and expression of individual genes but to changes in interactions among these genes as well. We examined co-expression changes in gene-network structure occurring during the onset and progression of nine different cancer types. Network complexity is generally reduced in the transition from normal precursor tissues to corresponding primary tumors. Cross-tissue cancer network similarity generally increases in early-stage cancers followed by a subsequent loss in cross-tissue cancer similarity as tumors reacquire cancer-specific network complexity. Gene-gene connections remaining stable through cancer development are enriched for “housekeeping” gene functions, whereas newly acquired interactions are associated with established cancer-promoting functions. Surprisingly, >90% of changes in gene-gene network interactions in cancers are not associated with changes in the expression of network genes relative to normal precursor tissues.

A computational approach to generate highly conserved gene co-expression networks with RNA-seq data

“…Overall, we found that genes displaying significant changes in gene-gene connections in the transition from normal to cancer cells were often not differentially expressed. This observation was consistent across different cancer types [Figure 2 in ( Arshad and McDonald, 2021 )].…”

“…Of these, 297 edges in cancer and 2261 edges in normal were consistently found in each model iteration and these conserved edge lists were used to define our final consensus networks. Figure 1 in the study by ( Arshad and McDonald, 2021 ) presents results for this overall reduction in network complexity observed in nine different cancer types, including LUSC.…”

“…This protocol describes the specific steps for differential co-expression analysis of lung squamous cell carcinoma (LUSC) samples ( Hammerman et al., 2012 ) from TCGA (dbGaP:phs000178.v11.p8) ( Weinstein et al, 2013 ) and healthy lung tissue samples from GTEx (dbGaP:phs000424.v8.p2) ( Ardlie et al, 2015 ). We have also used this protocol to analyze eight other cancer types (Glioblastoma multiforme-GMB, thyroid carcinoma-THCA, breast-BRCA, lung adenocarcinoma-LUAD, skin cutaneous melanoma-SKCM, kidney renal clear cell carcinoma-KIRC, ovarian-OV, acute myeloid leukemia-LAML) published in ( Arshad and McDonald, 2021 ).…”

“…For proof of concept, the protocol is used to leverage RNA-seq data of tumor samples from TCGA and healthy tissue samples from the GTEx database. For complete details on the use and execution of this protocol, please refer to Arshad and McDonald (2021) .…”

“…For complete details on the use and execution of this protocol, please refer to Arshad and McDonald (2021) .…”