I have normalized expression values of some genes in some patients and the how many days they have survived from their diagnosis with cancer like below

EDITED

I have taken mean of the expression values for each patient and I have how many days each patient survived since diagnosis as below

>

I done so but nothing I got

> fit <- survfit(Surv(time) ~ gene, data = km)
> print(fit)
Call: survfit(formula = Surv(time) ~ gene, data = km)

                  n events median 0.95LCL 0.95UCL
gene=-0.333140816 1      1    859      NA      NA
gene=-0.307846735 1      1    347      NA      NA
gene=-0.303559694 1      1   1339      NA      NA
gene=-0.290518776 1      1     61      NA      NA

The question is if these genes increase survival time or not but likely I am doing wrong

By your help

> fit <- coxph(Surv(time) ~ gene, data = km)
> print(fit)
Call:
coxph(formula = Surv(time) ~ gene, data = km)

       coef exp(coef) se(coef)     z     p
gene 0.8664    2.3783   1.4520 0.597 0.551

Likelihood ratio test=0.35  on 1 df, p=0.5543
n= 69, number of events= 69 

For plotting I guess I do need another information because

 library("survminer")

> ggsurvplot(fit, data = km)
Error in ggsurvplot(fit, data = a) : object 'ggsurv' not found

EDITED

I divided patients based on the median of survival days to Up and Down category like below but still I am failing to visualize that

 fit <- survfit(Surv(Time, Status) ~ Gene, data = km)


> ggsurvplot(fit, data = km)
Error in data.frame(..., check.names = FALSE) : 
  arguments imply differing number of rows: 70, 0, 140

Any help please?

Thanks