2 questions:

1. When use RNA-Seq data to do Differentially Expressed Genes (DEG) analysis, should the sample (/replicate) numbers of two groups must be the same? For example, if I have 8 samples from control, and 5 samples from treatment group, is it OK to use DESeq to do DEG analysis?

2. I'm using HISAT2 and featureCouts, after that, got the counts files, before putting them into DESeq, should I do normalisation firstly or can I use them directly?
I am expecting you are using DESeq2 and not DESeq.

 - As far as numbers in each group is concerned, it is pretty fine to
   perform DE analysis. The ideal scenario you get equal samples that
   are paired and you need to use that feature while performing DE
   analysis with any standard tool like DESeq2, edgeR or Limma.
 - DESeq2 can still perform DE analysis with just 2 samples in one group
   and 3 in the other. That's the lowest limit, going lower than that
   the results are usually not trusted worthy.

 

 - I have read edgeR can do even with lesser samples in the group but I
   do not trust such analysis tbh.  Your number of samples per group is
   pretty good to perform the tests.
 - About the normalization. The DE tools I mentioned and also you put in
   query work on count data. So there is no point of putting normalized 
   data in them. They will perform normalization in the subsequent
   steps. Just prepare your count table well and follow the DESeq2   
   tutorial and you are good to go.
 - I will advice to follow the tutorial pretty well before performing
   any DE analysis. It is always good to understand how the data behaves,
   not only a QC ploy but also a good practice in exploratory data analysis.
   Gives an understanding why you need to perform DE analysis and which samples
   should be included in it. There might be a scenario where you might
   have to move 1 or 2 samples from either of the group if they behave
   as outliers, owing to either batch or sequencing errors, even if you
   take care of them using batch correction methods. So a complete
   workflow is advised and also enables you to make a pipleline for discovery which you might be using more often in your lab setting. I hope this was informative for your query.