setwd(“D:\������ΰ�”)
data1 <- read.table(“GSE30219_lnc.txt”,stringsAsFactors = F)
data2 <- read.table(“GSE31210_lnc.txt”,stringsAsFactors = F)
data <- cbind(data1,data2)
linchuang1 <- read.csv(“clinical_GSE30219.csv”,stringsAsFactors = F)
linchuang2 <- read.csv(“clinical_GSE31210.csv”,stringsAsFactors = F)
linchuang1relapse_months[16:293]) / 12
linchuang2relapse_day / 365
colnames(linchuang1)[7] <- “dfs_time”
colnames(linchuang2)[7] <- “dfs_time”
linchuang <- rbind(linchuang1,linchuang2)

gradexunlian <- data1[“HCG26”,](-0.13431) + data1[“ENSG00000204261”,](-0.23298) + data1[“ENSG00000204282”,]_0.00089 + data1[“ENSG00000268001”,]_0.25595 + data1[“ENSG00000206337”,]_0.11689 + data1[“LOC286437”,](-0.32111) + data1[“LOC100996255”,](-0.05930)
gradeceshi <- data2[“HCG26”,](-0.13431) + data2[“ENSG00000204261”,](-0.23298) + data2[“ENSG00000204282”,]_0.00089 + data2[“ENSG00000268001”,]_0.25595 + data2[“ENSG00000206337”,]_0.11689 + data2[“LOC286437”,](-0.32111) + data2[“LOC100996255”,](-0.05930)
cutoff = -2.32953
risk_xunlian <- c()
risk_ceshi <- c()
for (i in 1:length(grade_xunlian)){
if (grade_xunlian[i] < cutoff){
risk_xunlian <- c(risk_xunlian,”low_risk”)
}
else {
risk_xunlian <- c(risk_xunlian,”high_risk”)
}
}
names(risk_xunlian) <- names(grade_xunlian)

for (i in 1:length(grade_ceshi)){
if (grade_ceshi[i] < cutoff){
risk_ceshi <- c(risk_ceshi,”low_risk”)
}
else {
risk_ceshi <- c(risk_ceshi,”high_risk”)
}
}
names(risk_ceshi) <- names(grade_ceshi)

risk <- c(risk_xunlian,risk_ceshi)

weizhi1 <- which(linchuang%0Aweizhi2%20%3C-%20which(linchuang#card=math&code=stage%20%3D%3D%20%22I%2FII%22%29%0Aweizhi2%20%3C-%20which%28linchuang)stage == “III/IV”)

data_zaoqi <- data[,linchuangSeries_sample_id[weizhi2]]

linchuang_zaoqi <- linchuang[weizhi1,]
linchuang_wanqi <- linchuang[weizhi2,]

risk_zaoqi <- risk[linchuangSeries_sample_id[weizhi2]]

library(survival)
library(survminer)
data_zaoqi <- data_zaoqi[,linchuang_zaoqiSeries_sample_id]

zaoqi_surv <- survfit(Surv(linchuang_zaoqirelapse)~risk_zaoqi,data = linchuang_zaoqi)
zaoqi_surv
survdiff(Surv(linchuang_zaoqirelapse)~risk_zaoqi,data = linchuang_zaoqi)
summary(zaoqi_surv)
ggsurvplot(surv,
pval = TRUE,#����Pֵ
risk.table = TRUE, # Add risk table
risk.table.col = “strata”, # Change risk table color by groups

linetype = “strata”, # Change line type by groups

surv.median.line = “hv”, # Specify median survival

ggtheme = theme_bw(), # Change ggplot2 theme

palette = c(red,blue),
conf.int = F,#������������
legend.labs=c(“high risk”,”low risk”)

wanqi_surv <- survfit(Surv(linchuang_wanqios_event)~risk_wanqi,data = linchuang_wanqi)
wanqi_surv
survdiff(Surv(linchuang_wanqirelapse)~risk_wanqi,data = linchuang_wanqi)
summary(wanqi_surv)
ggsurvplot(surv,
pval = TRUE,#����Pֵ
risk.table = TRUE, # Add risk table
risk.table.col = “strata”, # Change risk table color by groups

linetype = “strata”, # Change line type by groups

surv.median.line = “hv”, # Specify median survival

ggtheme = theme_bw(), # Change ggplot2 theme

palette = c(red,blue),
conf.int = F,#������������
legend.labs=c(“high risk”,”low risk”)