Dear BioStars Forum,

first of all I would like to thank the people in this forum for building this invaluable resource. I am still a beginner when it comes to RNA sequencing data analysis and my knowledge of proper methods is still rather limited. However, I feel that reading many of the answers on here has greatly helped my understanding so far.

I am currently working with the TCGA RNA sequencing datasets. In particular, I am inspecting several cohorts and am looking to perform differential gene expression analysis comparing the cancer samples to samples of normal tissue. Ideally, this would produce a list of differentially expressed genes for each of the inspected tumor types which I could use to further compare other results to. However, two factors complicate this intention:

1. Not all cohorts that I would like to investigate have normal tissue samples provided by the TCGA project. One such example would be the ACC (adrenocortical carcinoma) cohort.
2. There is concern that even for those cohorts for which normal tissue samples are provided, the normal samples may not be as useful because they were taken from tissue that appeared histologically normal but could be physiologically influenced by the tumor microenvironment (as mentioned here for example in this paper by Buzdin et al., 2019, section 10).

I was therefore wondering if it was possible to leverage RNA sequencing datasets for normal tissue from other databases (e.g. GEO, GTEx, etc.). I am aware that any such attempt of combining data from multiple studies would be heavily influenced by technical effects and differences between platforms and experimental strategies. Specifically I would be looking to include only such datasets that were sequenced on the same platform as their TCGA counterparts (Illumina) using at least similar experimental protocols. From what I understand, this would stil make such an analysis exploratory at best and simply irrelevant at worst.

In addition to the above I have read about several different approaches of normalizing data to account for such effects, always keeping in mind that they are likely to limit the interpretability of the results. From my understanding, simply including the different sources of data as a batch variable in a design matrix for the dfferential gene expression (as proposed for example in this question) might not be feasible here as this would make the batch variable linearly dependant on the group variable (healthy vs tumor) (as outlined in this guide, section 7.4). Please correct me, should I be wrongly informed on this. One approach that stood out to me as possibly promising to combine these datasets more meaningfully was the approach detailed by Molania et. al, 2022 called RUV-III PRPS.

My question now - given my limited experience and also possible lack of knowledge about proper methods - is as follows: Given my goal to procure a list of differentially expressed genes for different tumor types individually, is there any potential at all in combining TCGA datasets with normal tissue samples from other studies? If so, what metrics could I employ to build confidence in list of genes obtained through such merged data?

I would like to apologize in advance, should any of the above contain obvious lack of understanding of proper methods. I am looking forward to your comments and would like to thank you for your time and effort reading this question.