For a problem like this you want to look into the apply function in R. This function will let you perform a function row-wise or column-wise on a dataframe or matrix.

From the help menu: apply(X, MARGIN, FUN, ...) where X is your dataframe/matrix, MARGIN is either 1 for row-wise or 2 for column-wise, and FUN is the function that you want to perform. Depending on what you want to do, you can have a base FUN such as median or sum, or you can define your own function(x), where x is each row (or column) in your dataframe.

So for the example of dataframe df where columns 2-4 are Malignant and 5-7 are fibroblast you can run:

pValues <- apply(df, 1, function(x) t.test(x[2:4],x[5:7])$p.value)

This will take df and for each row (indicated by the 1, as opposed to each column) it will perform function(x), whereby a t-test is performed on the elements 2-4 compared to 5-7, and the p-value is reported (hence the $p.value). This will perform that function for each row, and store the p-values in the vector pValues.