Can we consider the loadings as a proxy for correlation, in a Principal Component Analysis (PCA)? In a PCA, the loadings can be understood as the weights for each original variable when calculating the principal component, or "how much each variable influence a principal component".
Thus, can we consider the loading value of each variable as how correlated the original variable is with the respective principal component? If a variable has a high loading value in a principal component, it necessarily has a high correlation with that same component? And if this variable presents a loading value of 1/-1, will it necessarily present a correlation of 1/-1 with the same principal component?

You can answer the questions yourself if you look at how the PCA is defined. For this, let $\mathbb{X}$ denote the $n\times p$ data matrix, and let $S = [s_{ij}]$ be the sample covariance matrix, e.g. $S = (n-1)^{-1} (\mathbb{X}^\top H \mathbb{X})$, where $H = I_n - \frac{1}{b}1_n1_n^\top$ is the centering matrix.
For simplicity, let's assume $\mathbb{X}$ has full rank. Consider the spectral decomposition of $S$,
$$S = \Gamma\Lambda \Gamma^\top,$$ where $\Gamma = [\gamma_1|\cdots|\gamma_p]$, $\Lambda = \text{diag}(\lambda_1,\ldots,\lambda_p)$, with $\gamma_i$ the $i$th eigenvector associated to the $i$the eigen value $\lambda_i$. The $i$th principal component (PC) is defined as
$$
y_i = X\gamma_i,
$$
and the sample product moment correlation between the $i$the PC and, say, $X_{j}$ the $j$th variable (i.e. the $j$the column of $\mathbb{X}$) is
$$r_{y_i, X_j} = \frac{S_{y_i,X_j}}{\sqrt{S_{y_i}^2 S_{X_j}^2}},$$
where $S_{y_i, X_j}$ is the sample covariance between $y_i$ and $X_j$. With some algebra, it is possible to show that
\begin{align*}
r_{y_i, X_j} = \frac{\gamma_{ij}\sqrt{\lambda_i}}{s_{kk}}, \text{for all }i,j\in\{1,\ldots,p\},\tag{*}
\end{align*}
where $\gamma_{ij}$ is the $j$the element of $\gamma_{i}$ and $s_{kk}$ is the standard deviation of $X_j$.
As you can see from (*), both the sign and the magnitude of the correlation are related to the loadings of the PCA, e.g. the eigenvectors of $S$. However, the correlation itself depends also on the variance of $X_j$ and on the $i$th eigenvalue. Thus a loading of $\pm 1$ doesn't necessarily imply a correlation equal to $\pm 1$. However, the higher the loading the higher the correlation, ceteris paribus.
Be careful when you interpret the results of a PCA, because the sign of the correlation is arbitrary, because if $\gamma_i$ is an eigenvector of $S$, then $-\gamma_i$ is also a valid eigenvector. When interpreting the PCA, indeed, we care about the sign and magnitude of loadings of a variable relative to others.
For instance, if $X_1$ and $X_2$  have got high loadings with a different sign, then we can say that they are correlated with the PC in question, with one variable being positively correlated and the other negatively correlated; but it is not meaningful to ask which one has got positive and which negative value since the sign is arbitrary.
