It sounds like you’ve already done differential expression analysis (with genes or transcripts) and a pathway enrichment analysis to find these. Differential expression of these functional groups is an interesting finding in its own right but there are several further investigations that could follow, depending on your biological research interests.

Clustering the expression patterns of the differentially expressed genes would be able to show if there are relationships between them (such as inter-gene correlations) or relationships to other phenotype data. This is commonly performed with unsupervised hierarchical clustering and displayed by a heatmap.

Another approach is pathway structure. There are many hierarchical annotation of gene functions such as gene ontology (GO terms) that overlap and are ranked: identifying smaller more specific pathways for future investigations. No pathway annotation is perfect so it’s best to replicate important findings across pathway databases (ideally different expression datasets as well). These functional groups of genes aren’t unordered: the structural relationships between pathways are well characterised in some cases. This includes many gene regulatory networks involving transcription factors. These graph structures are supported on databases such as WikiPathways and Reactome. Network analysis is relatively unexplored compared to differential expression.

While this is a bioinformatics question, there’s always a need to consider experimental validation as a future direction. If you already have a hypothesis based on your analysis, it may be more appropriate to design an experiment or contact experimental collaborators. If you have access to further genomics data, you could also explore how these differences are regulated: CAGE-Seq for enhancers and lncRNA, ATAC-Seq, and 3C/4C for chromatin structure, or Bisulfite-Seq and ChIP-Seq for epigenetic marks. An integrative analysis of expression and epigenomics data would be especially compelling and would likely account for different activity of transcription factor binding sites. Unfortunately, this can’t be done without more ‘omics data available but it would greatly benefit investigations of histones if it is feasible.

It sounds like you’ve already done differential expression analysis (with genes or transcripts) and a pathway enrichment analysis to find these. Differential expression of these functional groups is an interesting finding in its own right but there are several further investigations that could follow, depending on your biological research interests.

Clustering the expression patterns of the differentially expressed genes would be able to show if there are relationships between them (such as inter-gene correlations) or relationships to other phenotype data. This is commonly performed with unsupervised hierarchical clustering and displayed by a heatmap. You could also look into whether these differentially expressed genes are expressed in a tissue-specific manner. There is plenty of reference material available from ENCODE, FANTOM4/5, or GTEx that could be used for comparison (as well as cancer datasets) and the Human Cell Atlas Project (currently underway).

Another approach is pathway structure. There are many hierarchical annotation of gene functions such as gene ontology (GO terms) that overlap and are ranked: identifying smaller more specific pathways for future investigations. No pathway annotation is perfect so it’s best to replicate important findings across pathway databases (ideally different expression datasets as well). These functional groups of genes aren’t unordered: the structural relationships between pathways are well characterised in some cases. This includes many gene regulatory networks involving transcription factors. These graph structures are supported on databases such as WikiPathways and Reactome. Network analysis is relatively unexplored compared to differential expression.

While this is a bioinformatics question, there’s always a need to consider experimental validation as a future direction. If you already have a hypothesis based on your analysis, it may be more appropriate to design an experiment or contact experimental collaborators. If you have access to further genomics data, you could also explore how these differences are regulated: CAGE-Seq for enhancers and lncRNA, ATAC-Seq, and 3C/4C for chromatin structure, or Bisulfite-Seq and ChIP-Seq for epigenetic marks. An integrative analysis of expression and epigenomics data would be especially compelling and would likely account for different activity of transcription factor binding sites. Unfortunately, this can’t be done without more ‘omics data available but it would greatly benefit investigations of histones if it is feasible.