It depends what you want to know.

Comparing the gene expresison (under control conditions) between sexes will show you if the expression levels between the sexes are different, under control conditions.

You can do the same under treatment conditions.

If you want to know the difference in the treatment effects between the sexes, then you should compare the effects, which means to compare the expression differences treated vs. control. This is equivalent to the treatment x sex interaction.

Appreciate your response, I do not have normal samples and i want to compare male and female specific gene expression, then what should i do

without control, you'll just see the treatment influence on sex difference.

the optimal protocol should be:

treated male vs control male

treated female vs control female

treated male vs treated female

do not forget to have the most samples to analyze in each group, 3-5 is the minimum for statistical significance.

see GTEX portal for human to get an idea on how rich is the difference of gene expression between male and female

all the best

fred

Honestly, DESeq2 takes almost no time to run: it's by far the easiest part of the entire pipeline.

So...do it all. All comparisons.

Male treated vs male untreated -> effects of treatment on males specifically

Female treated vs female untreated -> effects of treatment on females specifically

Male and female treated vs male and female untreated -> sex-agnostic effects of treatment

Male untreated vs female untreated -> sex-specific differences under normal conditions

Male treated vs female treated -> sex-specific differences under treated conditions

male (all) vs female (all) -> treatment-agnostic effect of sex

What you'd hope is that the same genes would crop up under the same sort of comparisons (i.e. the best hits for male treated vs male untreated would be the same as those for female treated vs female untreated), and any genes that bucked the trend by being highly altered in males but not females (or vice versa) would also pop out of the other datasets, allowing you to determine whether the differences reflect sex alone, or are a consequence of sex + treatment.

RNAseq data always gives you far, far more candidates than you can realistically handle, so you can be quite aggressive in your culling of "interesting but not that interesting" candidates, but making multiple comparisons in this manner allows you to interpret your results with more nuance. And it's really quick and easy to do.