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1. I got the data of stomach cancer from TCGA, I found some different expression genes between cancer and normal sample.
2. I put aside my normal sample, and divide my cancer sample into train(80%) and test(20%) groups.
3. I did Unicox (with survival package), lasso (with glmnet package) and multicox ( with survival package) on train group, to find out which genes are more related to survival.
4. I use coefficients of each genes (that I got from multicox), to built my model and calculate the risk score. Like that: (expression of gene1×coefficient gene1 + expression of gene2×coefficient gene2 +...).
5. I divided my sample in to high risk and low risk groups, to compare survival between them, using Kaplan-Meier.

In training group, Kaplan-Meier shows significant difference between high and low risk group. However when I use my model on my test group, it is not significant. Why this problem occurs? I assumed my model is overfit of my train group, but how can I fix it? Why does this happen even when I use lasso?

1. I got the data of stomach cancer from TCGA, I found some different expression genes between cancer and normal sample.
2. I put aside my normal sample, and divide my cancer sample into train(80%) and test(20%) groups.
3. I did Unicox (with survival package), lasso (with glmnet package) and multicox ( with survival package) on train group, to find out which genes are more related to survival.
4. I use coefficients of each genes (that I got from multicox), to built my model and calculate the risk score. Like that: (expression of gene1×coefficient gene1 + expression of gene2×coefficient gene2 +...).
5. I divided my sample in to high risk and low risk groups, to compare survival between them, using Kaplan-Meier.

In training group, Kaplan-Meier shows significant difference between high and low risk group. However when I use my model on my test group, it is not significant. Why this problem occurs? I assumed my model is overfit of my train group, but how can I fix it? Why does this happen even when I use lasso?

1- I got the data of stomach cancer from TCGA, I found some different expression genes between cancer and normal sample.
2- I put aside my normal sample, and divide my cancer sample into train(80%) and test(20%) groups.
3- I did Unicox(with survival package), lasso (with glmnet package) and multicox ( with survival package) on train group, to find out which genes are more related to survival.
4- I use coefficients of each genes (that I got from multicox), to built my model and calculate the risk score. Like that: (expression of gene1×coefficient gene1 + expression of gene2×coefficient gene2 +...).
5- I divide my sample in to high risk and low risk groups, to compare survival between them, using kaplan-meier.

In training group, Kaplan-Meier shows significant difference between high and low risk group. However when I use my model on my test group, it is not significant. Why this problem occurs? I assumed my model is overfit of my train group, but how can I fix it? Why this happen even when I use lasso?

1. I got the data of stomach cancer from TCGA, I found some different expression genes between cancer and normal sample.
2. I put aside my normal sample, and divide my cancer sample into train(80%) and test(20%) groups.
3. I did Unicox (with survival package), lasso (with glmnet package) and multicox ( with survival package) on train group, to find out which genes are more related to survival.
4. I use coefficients of each genes (that I got from multicox), to built my model and calculate the risk score. Like that: (expression of gene1×coefficient gene1 + expression of gene2×coefficient gene2 +...).
5. I divided my sample in to high risk and low risk groups, to compare survival between them, using Kaplan-Meier.

In training group, Kaplan-Meier shows significant difference between high and low risk group. However when I use my model on my test group, it is not significant. Why this problem occurs? I assumed my model is overfit of my train group, but how can I fix it? Why does this happen even when I use lasso?

1- I got the data of stomach cancer from TCGA, I found some different expression genes between cancer and normal sample. 2- I put aside my normal sample, and divide my cancer sample into train(80%) and test(20%) groups. 3- I did Unicox(with survival package), lasso (with glmnet package) and multicox ( with survival package) on train group, to find out which genes are more related to survival. 4- I use coefficients of each genes (that I got from multicox), to built my model and calculate the risk score. Like that: (expression of gene1×coefficient gene1 + expression of gene2×coefficient gene2 +...). 5- I divide my sample in to high risk and low risk groups, to compare survival between them, using kaplan-meier. In training group, kaplan meier shows significant difference between high and low risk group. However when I use my model on my test group, it is not significant. Why this problem occurs? I assumed my model is overfit of my train group, but how can I fix it? Why this happen even when I use lasso??

1- I got the data of stomach cancer from TCGA, I found some different expression genes between cancer and normal sample. 
2- I put aside my normal sample, and divide my cancer sample into train(80%) and test(20%) groups. 
3- I did Unicox(with survival package), lasso (with glmnet package) and multicox ( with survival package) on train group, to find out which genes are more related to survival. 
4- I use coefficients of each genes (that I got from multicox), to built my model and calculate the risk score. Like that: (expression of gene1×coefficient gene1 + expression of gene2×coefficient gene2 +...).
5- I divide my sample in to high risk and low risk groups, to compare survival between them, using kaplan-meier.

In training group, Kaplan-Meier shows significant difference between high and low risk group. However when I use my model on my test group, it is not significant. Why this problem occurs? I assumed my model is overfit of my train group, but how can I fix it? Why this happen even when I use lasso?