I have performed WGCNA analyses on my count files. The WGCNA analysis gave me a module significantly related to tumor state. This module consists of 1700 genes, which I filtered by log2 fc, baseman expression, and excluding non-coding genes to 95 genes. Then, I imported them to perform a binominal univariate regression analysis. I gave the p-value cut-off (0.005) to univariate regression. The dependent variation was (cancer vs. normal), and the predictor was the 95 genes. This analysis just excluded one gene and exported 94 genes. Then, I imported the expression data of 94 genes to create a lasso regression model to know which genes better predict the tumor state. Finally, I calculated the AUC =1. Now I have two questions: 1- Why did univariate regression exclude only one gene? Is AUC=1 normal after performing lasso regression, and why has it happened?

these are my codes:

set.seed(2)

data<- as.data.frame(data)

index <- createDataPartition(data$result, p = 0.8, list = F, times = 1)

train_data <- data[index,]

test_data <- data[-index,]

train_data[] <- lapply(train_data, as.numeric)

x <- model.matrix( result ~ . ,
                   data = train_data)[,-1]

y <- train_data[,'result']


cv.model <- cv.glmnet(x = x,
                      y = y,
                      alpha = 1,
                      family = 'binomial',
                      nfolds = 10 )


lambda_min <- cv.model$lambda.min

lasso.model <- glmnet(x = x,
                      y = y,
                      alpha = 1,
                      family = 'binomial',
                      nfolds = 10,
                      lambda = lambda_min)

test_data[] <- lapply(test_data, as.numeric)

x2 <- model.matrix( result ~ . ,
                    data = test_data)[,-1]

y2 <- test_data[,'result']

auc_test <- assess.glmnet(lasso.model,           
                          newx = x2,              
                          newy = y2)$auc

auc_training <- assess.glmnet(lasso.model,           
                              newx = x,              
                              newy = y)$auc