Assuming $E[X_iX_j]=E[X_i]E[X_j]$ for i and j between 1 and n
Is proving that $var[X_1+X_2]=var[X_1]+var[X_2]$ sufficient in proving var($\sum\limits_{i=1}^n X_i $)=$\sum\limits_{i=1}^n var[X_i] $.
It seems pretty self-evident to me but perhaps do I need some intermediate steps to show this?
Let $X=\sum\limits_{i=1}^nX_i$. Then Var$(X)=E(X^2)-E(X)^2$.
Expand $X^2$ and take the expected value of it and then subtract the square of $E(X)=\sum\limits_{i=1}^nE(X_i)$.
The terms $2E(X_iX_j)-2E(X_i)E(X_j)$ will all cancel out leaving you with the sum of all the required variances. There is therefore no need to do the case of two variables first. Looking back at your proof you will then see why the result holds for any number of variables.