Write $B_x=B_x-B_s+B_s$. By linearity of conditional expectation and using the fact that $B_x-B_s$ is independent of $\mathcal F_s$, we are reduced to show that $E\left(\int_s^t(B_x-B_s)dx\mid\mathcal F_s\right)=0.$ It's enough to show that $\int_s^t(B_x-B_s)dx$ is independent of $\mathcal F_s$. To see that, write $\int_s^t(B_x-B_s)dx$ as the almost sure limit of $\frac 1n\sum_{j=0}^{n-1}(B_{s+xk/n}-B_s)$. If $X_n$ is independent of $Y$ for all $n$ and $X_n\to X$ almost surely, then $X$ and $Y$ are independent. Indeed if $O_1$ and $O_2$ are open, $P(X\in O_1,Y\in O_2)=\lim_{n\to +\infty}P(\bigcap_{j\geq n}X_n\in O_1,Y\in O_2)=\lim_{n\to +\infty}P(\bigcap_{j\geq n}X_n\in O_1)P(Y_n\in O_2),$ and we conclude by a monotone class argument (I should introduce the set of probability one where we have the convergence).