On the side of up-regulation, ube2t leads the pack, appearing in 8 out of 11 of the upregulation lists. Given the small size of our lists (typically about 200 genes), that's fairly impressive. A bit of googling reveals this gene is indeed implicated in lung cancer. At least one paper describes the development of a ube2t inhibitor, though this drug was injected in stomach tumors in mice. Genes up-regulated 7 times include anln, depdc1b, aspm, nek2, cenpf, stil, top2a, prom2, c16orf59, and melk. In general, the list is heavy with cell-cycle regulators. Wikipedia points out that given melk's abundance in cancers, attempts have been made to inhibit it; however, a crispr study casts doubt on melk's necessity in cancers. Perhaps melk is just along for the ride in a swarm of cell-cycle-related transcripts. Plugging melk into our co-expression app, that seems to be the case, with prominent cell cycle regulators like top2a, cdk1, aurkb, and more swarming alongside melk with extreme significance.*
Going one step further, we can plug the list of melk-coexpressed genes into our Fisher app. There, relevance to the cell cycle is obvious. For example, genes downregulated in a myeloma line on cdk4/6 inhibition overlap the melk-coexpression list with a log(P-value) of -133. Genes upregulated in S phase vs G1 phase in fibroblasts overlap with a value of -130. Etc. Thus we see how melk can be prominently upregulated in lung cancer without being a necessity.
To complicate matters, we plugged melk into the Regulation app. We have three studies in which melk was specifically targeted (one drug study, two knockdown studies). In the drug study, melk inhibition strongly downregulates (log(P) = -25) the genes with which melk swarms, while this effect was not seen in the two knockdown studies. We note that the drug study involved glioma stem cells.
Looking at down-regulation, the presence of ca4, carbonic anhydrase, in the list is quite impressive; 9 appearances in the 11 studies. There are papers on the role of ca4 in cancer, though there's not a high level of enthusiasm for developing ca4-agonists as cancer therapies. Entering the gene in our "Relevant Studies" app, we see two studies in which dexamethasone seems to do a nice job of upregulating ca4. Then again, we see a paper showing a positive link with lung cancer dexamethasone treatment and metastasis. Studies where primary cancer treatment apparently enhances the transcriptome you'd expect in metastasis are common in our database; perhaps we should do a deep dive on this subject. It's not as if an inverse correlation between primary cancer treatment and metastasis has not been noted; look here.
Genes appearing 8 times in the down-regulation list include fmo2, cav1, tcf21, fam107a, and rage.
Plugging the lung cancer up/down-regulation lists into the "Match Studies" app and selecting "inverse correlations" to search for means by which the lung cancer transcriptome could be reversed, the most prominent result is a mouse study in which the thymus transcriptome was altered via full body radiation (log(P) = -206). A study involving bmp2 treatment of MSCs ranks second (-186). Restricting "cell type" to lungs, the best lung-cancer reverser involves a MAPK inhibitor (see here). Not surprisingly, the treatment targets the cell cycle. Studies involving lactoferrin, erlotinib, etoposide, and more figure prominently in the list of lung cancer reversers, at least at the cell-line level.
*In fact, in some cases the significance is so extreme that our app won't output a log(P-value). It seems that P values below about 10^-320 don't get output, resulting in blank cells in our "log10 binomials" column. We're not particularly motivated to find a workaround for this issue, as we'd say that 10^-320 is pretty damn significant.
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