Understanding input of LSTM
LSTM layers work on 3D data with the following structure (nb_sequence, nb_timestep, nb_feature).


nb_sequence corresponds to the total number of sequences in your dataset (or to the batch size if you are using mini-batch learning).
nb_timestep corresponds to the size of your sequences.
nb_feature corresponds to number of features describing each of your timesteps.


Thus an LSTM layer will work like this : 
Let $t_i$ be the $i^{th}$ timestep of sequence $seq_j$, with $i \in [0, nb\_timestep], \ j \in [0, nb\_sequence]$.
An LSTM layer will make a prediction $p_i$ according to the $nb\_feature$ descriptors of $t_i$ with respect to its hidden state which is a representation of the timesteps $t_0$ to $t_{i-1}$.



Now, let's see what this means for your two configurations. For the sake of the explanation, I will suppose that we have sequences of words


For (53394, 3, 1) the LSTM will work on 53394 different sequences. Each sequence is 3 words long and each word is described through one feature only. For the first word of each sequence, the LSTM will make a prediction on its sole descriptor. For the second word, the prediction will be done from the unique descriptor with respect to what the first word was. Finally, for the third and last word in the sequence, the LSTM will emit a prediction from the unique descriptor with respect to what the two previous words were. Then, the LSTM begins the process anew for the following sequence.
For (53394, 1, 3), your sequences contain only one word which is described through 3 features. Sequences of one word are not really sequences, so the LSTM layer will not be useful in this case.


Hope it clears up how data are fed to an LSTM !



NB. Not related to the question but it may help : from your original shape, it seems your dataset contains 53394 words described with 3 features. If I am right, you would need a 3D shape like (53394, nb_timestep, 3) but with $nb\_timestep \neq 1 $. What you need then, is to define some window instead of reshaping your data.