# When using glmnet how to report p-value significance to claim significance of predictors?

I have a large set of predictors (more than 43,000) for predicting a dependent variable which can take 2 values (0 or 1). The number of observations is more than 45,000. Most of the predictors are unigrams, bigrams and trigrams of words, so there is high degree of collinearity among them. There is a lot of sparsity in my dataset as well. I am using the logistic regression from the glmnet package, which works for the kind of dataset I have. My problem is how can I report p-value significance of the predictors. I do get the beta coefficient, but is there a way to claim that the beta coefficients are statistically significant?

Here is my code:

library('glmnet')