RNAseq data analysis
The TCGAbiolinks reference manual provides a clear explanation of the two methods:


  Normalization for RNA-Seq Numerical and graphical summaries of RNA-Seq
  read data. Withinlane normalization procedures to adjust for
  GC-content effect (or other gene-level effects) on read counts: loess
  robust local regression, global-scaling, and full-quantile
  normalization (Risso et al., 2011). Between-lane normalization
  procedures to adjust for distributional differences between lanes
  (e.g., sequencing depth): global-scaling and full-quantile
  normalization (Bullard et al., 2010).


Ideally, you would know if your samples spanned different lanes or not, which would indicate which you should use - "geneLength" if so, "gcContent" if not. If you're using TCGA data, that metadata is likely available somewhere.