Survival Analysis

Colorectal Cancer | Necroptosis | Unique Genes per RCD Type | Gene Expression of Normal Samples

I. Preliminaries

Loading libraries

library("tidyverse")
library("tibble")
library("msigdbr")
library("ggplot2")
library("TCGAbiolinks")
library("RNAseqQC")
library("DESeq2")
library("ensembldb")
library("purrr")
library("magrittr")
library("vsn")
library("matrixStats")
library("dplyr")
library("grex")
library("survminer")
library("survival")

II. Downloading the TCGA gene expression data

Create a function for downloading TCGA gene expression data.

For more detailed documentation, refer to 2. Differential Gene Expression Analysis - TCGA.Rmd.

query_and_filter_samples <- function(project) {
  query_tumor <- GDCquery(
    project = project,
    data.category = "Transcriptome Profiling",
    data.type = "Gene Expression Quantification",
    experimental.strategy = "RNA-Seq",
    workflow.type = "STAR - Counts",
    access = "open",
    sample.type = "Primary Tumor"
  )
  tumor <- getResults(query_tumor)

  query_normal <- GDCquery(
    project = project,
    data.category = "Transcriptome Profiling",
    data.type = "Gene Expression Quantification",
    experimental.strategy = "RNA-Seq",
    workflow.type = "STAR - Counts",
    access = "open",
    sample.type = "Solid Tissue Normal"
  )
  normal <- getResults(query_normal)

  submitter_ids <- inner_join(tumor, normal, by = "cases.submitter_id") %>%
    dplyr::select(cases.submitter_id)
  tumor <- tumor %>%
    dplyr::filter(cases.submitter_id %in% submitter_ids$cases.submitter_id)
  normal <- normal %>%
    dplyr::filter(cases.submitter_id %in% submitter_ids$cases.submitter_id)

  samples <- rbind(tumor, normal)
  unique(samples$sample_type)

  query_project <- GDCquery(
    project = project,
    data.category = "Transcriptome Profiling",
    data.type = "Gene Expression Quantification",
    experimental.strategy = "RNA-Seq",
    workflow.type = "STAR - Counts",
    access = "open",
    sample.type = c("Solid Tissue Normal", "Primary Tumor"),
    barcode = as.list(samples$sample.submitter_id)
  )

  # If this is your first time running this notebook (i.e., you have not yet downloaded the results of the query in the previous block),
  # uncomment the line below

  # GDCdownload(query_project)

  return(list(samples = samples, query_project = query_project))
}

Download the TCGA gene expression data for colorectal cancer (TCGA-COAD).

projects <- c("TCGA-COAD")

with_results_projects <- c()

samples <- list()
project_data <- list()

for (project in projects) {
  result <- tryCatch(
    {
      result <- query_and_filter_samples(project)
      samples[[project]] <- result$samples
      project_data[[project]] <- result$query_project

      with_results_projects <- c(with_results_projects, project)
    },
    error = function(e) {

    }
  )
}

Running the code block above should generate and populate a directory named GDCdata.

III. Data preprocessing

Construct the RNA-seq count matrix for each cancer type.

tcga_data <- list()
tcga_matrix <- list()

projects <- with_results_projects
for (project in projects) {
  tcga_data[[project]] <- GDCprepare(project_data[[project]], summarizedExperiment = TRUE)
}

for (project in projects) {
  count_matrix <- assay(tcga_data[[project]], "unstranded")

  # Remove duplicate entries
  count_matrix_df <- data.frame(count_matrix)
  count_matrix_df <- count_matrix_df[!duplicated(count_matrix_df), ]
  count_matrix <- data.matrix(count_matrix_df)
  rownames(count_matrix) <- cleanid(rownames(count_matrix))
  count_matrix <- count_matrix[!(duplicated(rownames(count_matrix)) | duplicated(rownames(count_matrix), fromLast = TRUE)), ]

  tcga_matrix[[project]] <- count_matrix
}

Format the samples table so that it can be fed as input to DESeq2.

for (project in projects) {
  rownames(samples[[project]]) <- samples[[project]]$cases
  samples[[project]] <- samples[[project]] %>%
    dplyr::select(case = "cases.submitter_id", type = "sample_type")
  samples[[project]]$type <- str_replace(samples[[project]]$type, "Solid Tissue Normal", "normal")
  samples[[project]]$type <- str_replace(samples[[project]]$type, "Primary Tumor", "tumor")
}

DESeq2 requires the row names of samples should be identical to the column names of count_matrix.

for (project in projects) {
  colnames(tcga_matrix[[project]]) <- gsub(x = colnames(tcga_matrix[[project]]), pattern = "\\.", replacement = "-")
  tcga_matrix[[project]] <- tcga_matrix[[project]][, rownames(samples[[project]])]

  # Sanity check
  print(all(colnames(tcga_matrix[[project]]) == rownames(samples[[project]])))
}

IV. Differential gene expression analysis

For more detailed documentation on obtaining the gene set, refer to 7. Differential Gene Expression Analysis - TCGA - Pan-cancer - Unique Genes.Rmd.

RCDdb <- "temp/unique_genes/necroptosis_ferroptosis_pyroptosis/"

Write utility functions for filtering the gene sets, performing differential gene expression analysis, plotting the results, and performing variance-stabilizing transformation.

filter_gene_set_and_perform_dgea <- function(genes) {
  tcga_rcd <- list()

  for (project in projects) {
    rownames(genes) <- genes$gene_id
    tcga_rcd[[project]] <- tcga_matrix[[project]][rownames(tcga_matrix[[project]]) %in% genes$gene_id, ]
    tcga_rcd[[project]] <- tcga_rcd[[project]][, rownames(samples[[project]])]
  }

  dds_rcd <- list()
  res_rcd <- list()

  for (project in projects) {
    print(project)
    print("=============")
    dds <- DESeqDataSetFromMatrix(
      countData = tcga_rcd[[project]],
      colData = samples[[project]],
      design = ~type
    )
    dds <- filter_genes(dds, min_count = 10)
    dds$type <- relevel(dds$type, ref = "normal")
    dds_rcd[[project]] <- DESeq(dds)
    res_rcd[[project]] <- results(dds_rcd[[project]])
  }

  deseq.bbl.data <- list()

  for (project in projects) {
    deseq.results <- res_rcd[[project]]
    deseq.bbl.data[[project]] <- data.frame(
      row.names = rownames(deseq.results),
      baseMean = deseq.results$baseMean,
      log2FoldChange = deseq.results$log2FoldChange,
      lfcSE = deseq.results$lfcSE,
      stat = deseq.results$stat,
      pvalue = deseq.results$pvalue,
      padj = deseq.results$padj,
      cancer_type = project,
      gene_symbol = genes[rownames(deseq.results), "gene"]
    )
  }

  deseq.bbl.data.combined <- bind_rows(deseq.bbl.data)
  deseq.bbl.data.combined <- dplyr::filter(deseq.bbl.data.combined, abs(log2FoldChange) >= 1.5 & padj < 0.05)

  return(deseq.bbl.data.combined)
}

plot_dgea <- function(deseq.bbl.data.combined) {
  sizes <- c("<10^-15" = 4, "10^-10" = 3, "10^-5" = 2, "0.05" = 1)

  deseq.bbl.data.combined <- deseq.bbl.data.combined %>%
    mutate(fdr_category = cut(padj,
      breaks = c(-Inf, 1e-15, 1e-10, 1e-5, 0.05),
      labels = c("<10^-15", "10^-10", "10^-5", "0.05"),
      right = FALSE
    ))

  top_genes <- deseq.bbl.data.combined %>%
    group_by(cancer_type) %>%
    mutate(rank = rank(-abs(log2FoldChange))) %>%
    dplyr::filter(rank <= 10) %>%
    ungroup()

  ggplot(top_genes, aes(y = cancer_type, x = gene_symbol, size = fdr_category, fill = log2FoldChange)) +
    geom_point(alpha = 0.5, shape = 21, color = "black") +
    scale_size_manual(values = sizes) +
    scale_fill_gradient2(low = "blue", mid = "white", high = "red", limits = c(min(deseq.bbl.data.combined$log2FoldChange), max(deseq.bbl.data.combined$log2FoldChange))) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(size = 9, angle = 90, hjust = 1)
    ) +
    theme(legend.position = "bottom") +
    theme(legend.position = "bottom") +
    labs(size = "Adjusted p-value", fill = "log2 FC", y = "Cancer type", x = "Gene")
}

perform_vsd <- function(genes) {
  tcga_rcd <- list()

  for (project in projects) {
    rownames(genes) <- genes$gene_id
    tcga_rcd[[project]] <- tcga_matrix[[project]][rownames(tcga_matrix[[project]]) %in% genes$gene_id, ]
    tcga_rcd[[project]] <- tcga_rcd[[project]][, rownames(samples[[project]])]
  }

  vsd_rcd <- list()

  for (project in projects) {
    print(project)
    print("=============")
    dds <- DESeqDataSetFromMatrix(
      countData = tcga_rcd[[project]],
      colData = samples[[project]],
      design = ~type
    )
    dds <- filter_genes(dds, min_count = 10)

    # Perform variance stabilization
    dds <- estimateSizeFactors(dds)
    nsub <- sum(rowMeans(counts(dds, normalized = TRUE)) > 10)
    vsd <- vst(dds, nsub = nsub)
    vsd_rcd[[project]] <- assay(vsd)
  }

  return(vsd_rcd)
}

Necroptosis

Fetch the gene set of interest.

genes <- read.csv(paste0(RCDdb, "Necroptosis.csv"))
print(genes)

genes$gene_id <- cleanid(genes$gene_id)
genes <- distinct(genes, gene_id, .keep_all = TRUE)
genes <- subset(genes, gene_id != "")
genes

Filter the genes to include only those in the gene set of interest, and then perform differential gene expression analysis.

deseq.bbl.data.combined <- filter_gene_set_and_perform_dgea(genes)

[1] "TCGA-COAD"
[1] "============="

Warning: some variables in design formula are characters, converting to factorsestimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 1 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing

deseq.bbl.data.combined

Plot the results.

plot_dgea(deseq.bbl.data.combined)

Perform variance-stabilizing transformation for further downstream analysis (i.e., for survival analysis).

vsd <- perform_vsd(genes)

[1] "TCGA-COAD"
[1] "============="

V. Downloading the clinical data

Download clinical data from TCGA, and perform some preprocessing: - The deceased column should be FALSE if the patient is alive and TRUE otherwise - The overall_survival column should reflect the follow-up time if the patient is alive and the days to death otherwise

download_clinical_data <- function(project) {
  clinical_data <- GDCquery_clinic(project)
  clinical_data$deceased <- ifelse(clinical_data$vital_status == "Alive", FALSE, TRUE)
  clinical_data$overall_survival <- ifelse(clinical_data$vital_status == "Alive",
    clinical_data$days_to_last_follow_up,
    clinical_data$days_to_death
  )

  return(clinical_data)
}

tcga_clinical <- list()
for (project in projects) {
  tcga_clinical[[project]] <- download_clinical_data(project)
}

VI. Performing survival analysis

Write utility functions for performing survival analysis.

construct_gene_df <- function(gene_of_interest, project) {
  gene_df <- vsd[[project]] %>%
    as.data.frame() %>%
    rownames_to_column(var = "gene_id") %>%
    gather(key = "case_id", value = "counts", -gene_id) %>%
    left_join(., genes, by = "gene_id") %>%
    dplyr::filter(gene == gene_of_interest) %>%
    dplyr::filter(case_id %in% rownames(samples[[project]] %>% dplyr::filter(type == "normal")))

  q1 <- quantile(gene_df$counts, probs = 0.25)
  q3 <- quantile(gene_df$counts, probs = 0.75)
  gene_df$strata <- ifelse(gene_df$counts >= q3, "HIGH", ifelse(gene_df$counts <= q1, "LOW", "MIDDLE"))
  gene_df <- gene_df %>% dplyr::filter(strata %in% c("LOW", "HIGH"))
  gene_df$case_id <- paste0(sapply(strsplit(as.character(gene_df$case_id), "-"), `[`, 1), '-',
                          sapply(strsplit(as.character(gene_df$case_id), "-"), `[`, 2), '-', 
                          sapply(strsplit(as.character(gene_df$case_id), "-"), `[`, 3))
  gene_df <- merge(gene_df, tcga_clinical[[project]], by.x = "case_id", by.y = "submitter_id")
  
  return(gene_df)
}

compute_surival_fit <- function(gene_df) {
  return (survfit(Surv(overall_survival, deceased) ~ strata, data = gene_df))
}

compute_cox <- function(gene_df) {
  return (coxph(Surv(overall_survival, deceased) ~ strata, data=gene_df))
}

plot_survival <- function(fit) {
  return(ggsurvplot(fit,
    data = gene_df,
    pval = T,
    risk.table = T,
    risk.table.height = 0.3
  ))
}

compute_survival_diff <- function(gene_df) {
  return(survdiff(Surv(overall_survival, deceased) ~ strata, data = gene_df))
}

Perform survival analysis by testing for the difference in the Kaplan-Meier curves using the G-rho family of Harrington and Fleming tests: https://rdrr.io/cran/survival/man/survdiff.html

MLKL is the primary executor of necroptosis.

significant_projects <- c()
significant_genes <- c()

ctr <- 1
for (project in projects) {
  for (gene in c("MLKL", genes$gene)) {
    cat(project, gene, "\n\n")
    tryCatch (
      {
        gene_df <- construct_gene_df(gene, project)
      },
      error = function(e) {
      }
    )

    if (nrow(gene_df) > 0) {
      fit <- compute_surival_fit(gene_df)
      tryCatch (
        {
          survival <- compute_survival_diff(gene_df)
          cox <- compute_cox(gene_df)
          print(ctr)
          ctr <- ctr + 1
          print(survival)
          cat("\n")
          print(cox)
          print(plot_survival(fit))
          if (pchisq(survival$chisq, length(survival$n)-1, lower.tail = FALSE) < 0.05) {
            significant_projects <- c(significant_projects, project)
            significant_genes <- c(significant_genes, gene)
          }
        },
        error = function(e) {
        }
      )
      
    }
    
    cat("\n\n============================\n\n")
  }
}

TCGA-COAD MLKL 

[1] 1
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        6     5.35    0.0781     0.242
strata=LOW  11        3     3.65    0.1147     0.242

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.3753    0.6871   0.7675 -0.489 0.625

Likelihood ratio test=0.24  on 1 df, p=0.6228
n= 22, number of events= 9 


============================

TCGA-COAD RBCK1 

[1] 2
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        7     4.92     0.882      2.12
strata=LOW  11        3     5.08     0.853      2.12

 Chisq= 2.1  on 1 degrees of freedom, p= 0.1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -1.1392    0.3201   0.8201 -1.389 0.165

Likelihood ratio test=2.25  on 1 df, p=0.1338
n= 22, number of events= 10 


============================

TCGA-COAD JAK2 

[1] 3
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     2.52     0.874      1.38
strata=LOW  11        3     4.48     0.491      1.38

 Chisq= 1.4  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.8750    0.4168   0.7682 -1.139 0.255

Likelihood ratio test=1.31  on 1 df, p=0.2522
n= 22, number of events= 7 


============================

TCGA-COAD ZBP1 

[1] 4
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     5.04  0.000314   0.00094
strata=LOW  11        3     2.96  0.000534   0.00094

 Chisq= 0  on 1 degrees of freedom, p= 1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z     p
strataLOW 0.02359   1.02387  0.76968 0.031 0.976

Likelihood ratio test=0  on 1 df, p=0.9756
n= 22, number of events= 8 


============================

TCGA-COAD RNF31 

[1] 5
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     3.38     0.113     0.224
strata=LOW  11        3     3.62     0.105     0.224

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.3641    0.6948   0.7733 -0.471 0.638

Likelihood ratio test=0.22  on 1 df, p=0.6359
n= 22, number of events= 7 


============================

TCGA-COAD IFNB1 

[1] 6
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     3.38     0.113     0.224
strata=LOW  11        3     3.62     0.105     0.224

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.3641    0.6948   0.7733 -0.471 0.638

Likelihood ratio test=0.22  on 1 df, p=0.6359
n= 22, number of events= 7 


============================

TCGA-COAD TRAF5 

[1] 7
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.53     0.614      1.39
strata=LOW  11        3     4.47     0.485      1.39

 Chisq= 1.4  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.8894    0.4109   0.7774 -1.144 0.253

Likelihood ratio test=1.32  on 1 df, p=0.2503
n= 22, number of events= 8 


============================

TCGA-COAD BIRC2 

[1] 8
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        1     2.46     0.863      1.71
strata=LOW  11        5     3.54     0.598      1.71

 Chisq= 1.7  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

           coef exp(coef) se(coef)     z     p
strataLOW 1.361     3.899    1.121 1.214 0.225

Likelihood ratio test=1.84  on 1 df, p=0.1753
n= 22, number of events= 6 


============================

TCGA-COAD TRAF2 

[1] 9
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     5.59    0.0628     0.247
strata=LOW  11        3     2.41    0.1459     0.247

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.4058    1.5004   0.8222 0.494 0.622

Likelihood ratio test=0.24  on 1 df, p=0.6228
n= 22, number of events= 8 


============================

TCGA-COAD BCL2 

[1] 10
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     3.72    0.0217    0.0546
strata=LOW  11        3     3.28    0.0246    0.0546

 Chisq= 0.1  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.1912    0.8260   0.8191 -0.233 0.815

Likelihood ratio test=0.05  on 1 df, p=0.8156
n= 22, number of events= 7 


============================

TCGA-COAD STAT4 

[1] 11
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        1     2.39      0.81      2.14
strata=LOW  11        3     1.61      1.20      2.14

 Chisq= 2.1  on 1 degrees of freedom, p= 0.1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

           coef exp(coef) se(coef)     z     p
strataLOW 1.608     4.995    1.196 1.345 0.179

Likelihood ratio test=2.14  on 1 df, p=0.1432
n= 22, number of events= 4 


============================

TCGA-COAD BIRC3 

[1] 12
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     3.04     0.353     0.741
strata=LOW  11        4     2.96     0.361     0.741

 Chisq= 0.7  on 1 degrees of freedom, p= 0.4 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.7432    2.1026   0.8814 0.843 0.399

Likelihood ratio test=0.75  on 1 df, p=0.3851
n= 22, number of events= 6 


============================

TCGA-COAD STAT1 

[1] 13
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.17      1.06      2.77
strata=LOW  11        1     2.83      1.19      2.77

 Chisq= 2.8  on 1 degrees of freedom, p= 0.1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z     p
strataLOW -1.6717    0.1879   1.1220 -1.49 0.136

Likelihood ratio test=2.86  on 1 df, p=0.09107
n= 22, number of events= 6 


============================

TCGA-COAD STAT2 

[1] 14
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.09      1.17      1.97
strata=LOW  11        3     4.91      0.74      1.97

 Chisq= 2  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -1.0018    0.3672   0.7409 -1.352 0.176

Likelihood ratio test=1.91  on 1 df, p=0.1674
n= 22, number of events= 8 


============================

TCGA-COAD TNFSF10 

[1] 15
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     4.68     0.606      1.29
strata=LOW  11        7     5.32     0.534      1.29

 Chisq= 1.3  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.7912    2.2060   0.7133 1.109 0.267

Likelihood ratio test=1.31  on 1 df, p=0.2527
n= 22, number of events= 10 


============================

TCGA-COAD TYK2 

[1] 16
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     3.18     0.213     0.392
strata=LOW  11        3     3.82     0.177     0.392

 Chisq= 0.4  on 1 degrees of freedom, p= 0.5 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z     p
strataLOW -0.4752    0.6218   0.7658 -0.62 0.535

Likelihood ratio test=0.39  on 1 df, p=0.5322
n= 22, number of events= 7 


============================

TCGA-COAD PPIA 

[1] 17
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     1.94   0.00203   0.00394
strata=LOW  11        3     3.06   0.00129   0.00394

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

              coef exp(coef) se(coef)      z    p
strataLOW -0.06283   0.93910  1.00050 -0.063 0.95

Likelihood ratio test=0  on 1 df, p=0.9499
n= 22, number of events= 5 


============================

TCGA-COAD TNFRSF1A 

[1] 18
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     2.64      2.10      4.04
strata=LOW  11        2     4.36      1.28      4.04

 Chisq= 4  on 1 degrees of freedom, p= 0.04 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z      p
strataLOW -1.9975    0.1357   1.1300 -1.768 0.0771

Likelihood ratio test=4.28  on 1 df, p=0.03856
n= 22, number of events= 7 


============================

TCGA-COAD CAPN2 

[1] 19
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     4.33     0.408     0.924
strata=LOW  11        6     4.67     0.378     0.924

 Chisq= 0.9  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.7005    2.0148   0.7425 0.943 0.345

Likelihood ratio test=0.92  on 1 df, p=0.3368
n= 22, number of events= 9 


============================

TCGA-COAD FAS 

[1] 20
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        1     2.68     1.050      1.91
strata=LOW  11        5     3.32     0.845      1.91

 Chisq= 1.9  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

           coef exp(coef) se(coef)     z     p
strataLOW 1.399     4.053    1.097 1.275 0.202

Likelihood ratio test=2.13  on 1 df, p=0.1448
n= 22, number of events= 6 


============================

TCGA-COAD PGAM5 

[1] 21
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     4.48    0.0611      0.22
strata=LOW  11        2     2.52    0.1084      0.22

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.4271    0.6524   0.9164 -0.466 0.641

Likelihood ratio test=0.22  on 1 df, p=0.6376
n= 22, number of events= 7 


============================

TCGA-COAD MLKL 

[1] 22
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        6     5.35    0.0781     0.242
strata=LOW  11        3     3.65    0.1147     0.242

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.3753    0.6871   0.7675 -0.489 0.625

Likelihood ratio test=0.24  on 1 df, p=0.6228
n= 22, number of events= 9 


============================

TCGA-COAD FADD 

[1] 23
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     2.81     0.508     0.993
strata=LOW  11        3     4.19     0.340     0.993

 Chisq= 1  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z     p
strataLOW -0.8604    0.4230   0.8868 -0.97 0.332

Likelihood ratio test=1.01  on 1 df, p=0.3157
n= 22, number of events= 7 


============================

TCGA-COAD TRPM7 

[1] 24
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     3.02     0.343     0.623
strata=LOW  11        5     3.98     0.260     0.623

 Chisq= 0.6  on 1 degrees of freedom, p= 0.4 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.6567    1.9283   0.8463 0.776 0.438

Likelihood ratio test=0.65  on 1 df, p=0.4205
n= 22, number of events= 7 


============================

TCGA-COAD FASLG 

[1] 25
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     3.26    0.0214    0.0478
strata=LOW  11        3     2.74    0.0256    0.0478

 Chisq= 0  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.1796    1.1968   0.8230 0.218 0.827

Likelihood ratio test=0.05  on 1 df, p=0.8273
n= 22, number of events= 6 


============================

TCGA-COAD TNFRSF10B 

[1] 26
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     5.65    0.0752     0.224
strata=LOW  11        4     3.35    0.1269     0.224

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.3379    1.4020   0.7172 0.471 0.638

Likelihood ratio test=0.22  on 1 df, p=0.6382
n= 22, number of events= 9 


============================

TCGA-COAD VPS4A 

[1] 27
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     4.31     0.398      1.05
strata=LOW  11        4     2.69     0.638      1.05

 Chisq= 1  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.7695    2.1587   0.7690 1.001 0.317

Likelihood ratio test=1.01  on 1 df, p=0.314
n= 22, number of events= 7 


============================

TCGA-COAD TNFRSF10A 

[1] 28
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     4.52    0.0595     0.156
strata=LOW  11        4     3.48    0.0772     0.156

 Chisq= 0.2  on 1 degrees of freedom, p= 0.7 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.3024    1.3531   0.7689 0.393 0.694

Likelihood ratio test=0.16  on 1 df, p=0.6926
n= 22, number of events= 8 


============================

TCGA-COAD GLUD1 

[1] 29
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     1.95     0.567     0.958
strata=LOW  11        3     4.05     0.273     0.958

 Chisq= 1  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.8832    0.4135   0.9291 -0.951 0.342

Likelihood ratio test=0.93  on 1 df, p=0.3352
n= 22, number of events= 6 


============================

TCGA-COAD EIF2AK2 

[1] 30
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     2.78     0.534         1
strata=LOW  11        2     3.22     0.461         1

 Chisq= 1  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.8431    0.4304   0.8681 -0.971 0.331

Likelihood ratio test=1.01  on 1 df, p=0.3154
n= 22, number of events= 6 


============================

TCGA-COAD CYLD 

[1] 31
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.87   0.00580   0.00987
strata=LOW  11        4     4.13   0.00403   0.00987

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

              coef exp(coef) se(coef)      z     p
strataLOW -0.07599   0.92683  0.76520 -0.099 0.921

Likelihood ratio test=0.01  on 1 df, p=0.921
n= 22, number of events= 7 


============================

TCGA-COAD SPATA2 

[1] 32
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     4.83     0.696      1.96
strata=LOW  11        5     3.17     1.062      1.96

 Chisq= 2  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 1.1225    3.0726   0.8418 1.333 0.182

Likelihood ratio test=1.99  on 1 df, p=0.158
n= 22, number of events= 8 


============================

TCGA-COAD DNM1L 

[1] 33
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.64     0.511      1.26
strata=LOW  11        2     3.36     0.553      1.26

 Chisq= 1.3  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.9414    0.3901   0.8681 -1.085 0.278

Likelihood ratio test=1.26  on 1 df, p=0.2613
n= 22, number of events= 7 


============================

TCGA-COAD CFLAR 

[1] 34
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     3.37     0.119     0.234
strata=LOW  11        3     3.63     0.110     0.234

 Chisq= 0.2  on 1 degrees of freedom, p= 0.6 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z    p
strataLOW -0.3719    0.6895   0.7722 -0.482 0.63

Likelihood ratio test=0.23  on 1 df, p=0.628
n= 22, number of events= 7 


============================

TCGA-COAD TICAM1 

[1] 35
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     3.56     0.687      1.73
strata=LOW  11        4     2.44     1.005      1.73

 Chisq= 1.7  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

           coef exp(coef) se(coef)     z     p
strataLOW 1.101     3.008    0.876 1.257 0.209

Likelihood ratio test=1.7  on 1 df, p=0.1922
n= 22, number of events= 6 


============================

TCGA-COAD HSP90AA1 

[1] 36
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.86   0.00664    0.0128
strata=LOW  11        3     3.14   0.00605    0.0128

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

              coef exp(coef) se(coef)      z    p
strataLOW -0.09246   0.91168  0.81875 -0.113 0.91

Likelihood ratio test=0.01  on 1 df, p=0.9101
n= 22, number of events= 6 


============================

TCGA-COAD IL33 

[1] 37
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.65     0.495      1.23
strata=LOW  11        2     3.35     0.541      1.23

 Chisq= 1.2  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.9320    0.3938   0.8691 -1.072 0.284

Likelihood ratio test=1.23  on 1 df, p=0.2669
n= 22, number of events= 7 


============================

TCGA-COAD IRF9 

[1] 38
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.96     0.274     0.495
strata=LOW  11        4     5.04     0.215     0.495

 Chisq= 0.5  on 1 degrees of freedom, p= 0.5 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.4708    0.6245   0.6750 -0.698 0.485

Likelihood ratio test=0.49  on 1 df, p=0.4837
n= 22, number of events= 9 


============================

TCGA-COAD SHARPIN 

[1] 39
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     4.85     0.149     0.598
strata=LOW  11        3     2.15     0.336     0.598

 Chisq= 0.6  on 1 degrees of freedom, p= 0.4 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.6968    2.0074   0.9189 0.758 0.448

Likelihood ratio test=0.59  on 1 df, p=0.4422
n= 22, number of events= 7 


============================

TCGA-COAD IFNAR1 

[1] 40
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.53    0.0868     0.177
strata=LOW  11        3     3.47    0.0633     0.177

 Chisq= 0.2  on 1 degrees of freedom, p= 0.7 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.3824    0.6823   0.9151 -0.418 0.676

Likelihood ratio test=0.18  on 1 df, p=0.6731
n= 22, number of events= 6 


============================

TCGA-COAD XIAP 

[1] 41
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     4.41    0.0389     0.103
strata=LOW  11        4     3.59    0.0478     0.103

 Chisq= 0.1  on 1 degrees of freedom, p= 0.7 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.2506    1.2848   0.7819 0.321 0.749

Likelihood ratio test=0.1  on 1 df, p=0.7476
n= 22, number of events= 8 


============================

TCGA-COAD VDAC3 

[1] 42
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.74    0.0242    0.0448
strata=LOW  11        3     3.26    0.0204    0.0448

 Chisq= 0  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.1729    0.8412   0.8179 -0.211 0.833

Likelihood ratio test=0.04  on 1 df, p=0.8327
n= 22, number of events= 6 


============================

TCGA-COAD CAMK2A 

[1] 43
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     3.92     0.300     0.603
strata=LOW  11        3     4.08     0.287     0.603

 Chisq= 0.6  on 1 degrees of freedom, p= 0.4 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.5668    0.5673   0.7389 -0.767 0.443

Likelihood ratio test=0.61  on 1 df, p=0.4356
n= 22, number of events= 8 


============================

TCGA-COAD VDAC1 

[1] 44
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     4.36     0.424      1.01
strata=LOW  11        5     3.64     0.508      1.01

 Chisq= 1  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.7426    2.1013   0.7533 0.986 0.324

Likelihood ratio test=1  on 1 df, p=0.3165
n= 22, number of events= 8 


============================

TCGA-COAD RIPK3 

[1] 45
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     4.36      1.28      2.88
strata=LOW  11        6     3.64      1.54      2.88

 Chisq= 2.9  on 1 degrees of freedom, p= 0.09 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 1.3072    3.6957   0.8235 1.587 0.112

Likelihood ratio test=2.95  on 1 df, p=0.0859
n= 22, number of events= 8 


============================

TCGA-COAD CAPN1 

[1] 46
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     3.31    0.0282    0.0666
strata=LOW  11        3     2.69    0.0345    0.0666

 Chisq= 0.1  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.2167    1.2420   0.8412 0.258 0.797

Likelihood ratio test=0.07  on 1 df, p=0.7969
n= 22, number of events= 6 


============================

TCGA-COAD USP21 

[1] 47
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     1.97  0.000522   0.00108
strata=LOW  11        2     2.03  0.000506   0.00108

 Chisq= 0  on 1 degrees of freedom, p= 1 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

              coef exp(coef) se(coef)      z     p
strataLOW -0.03366   0.96690  1.02466 -0.033 0.974

Likelihood ratio test=0  on 1 df, p=0.9738
n= 22, number of events= 4 


============================

TCGA-COAD AIFM1 

[1] 48
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     3.38    0.0433    0.0854
strata=LOW  11        4     3.62    0.0405    0.0854

 Chisq= 0.1  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.2249    1.2523   0.7711 0.292 0.771

Likelihood ratio test=0.09  on 1 df, p=0.7696
n= 22, number of events= 7 


============================

TCGA-COAD TRADD 

[1] 49
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3     2.73    0.0271    0.0638
strata=LOW  11        3     3.27    0.0226    0.0638

 Chisq= 0.1  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.2372    0.7888   0.9409 -0.252 0.801

Likelihood ratio test=0.06  on 1 df, p=0.8
n= 22, number of events= 6 


============================

TCGA-COAD OPTN 

[1] 50
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     3.89   0.00311   0.00611
strata=LOW  11        5     5.11   0.00237   0.00611

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

              coef exp(coef) se(coef)      z     p
strataLOW -0.05547   0.94604  0.70976 -0.078 0.938

Likelihood ratio test=0.01  on 1 df, p=0.9377
n= 22, number of events= 9 


============================

TCGA-COAD PPID 

[1] 51
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     2.22   0.02088     0.038
strata=LOW  11        5     4.78   0.00966     0.038

 Chisq= 0  on 1 degrees of freedom, p= 0.8 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

            coef exp(coef) se(coef)     z     p
strataLOW 0.1788    1.1958   0.9188 0.195 0.846

Likelihood ratio test=0.04  on 1 df, p=0.8449
n= 22, number of events= 7 


============================

TCGA-COAD RIPK1 

[1] 52
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     2.86     0.451     0.903
strata=LOW  11        4     5.14     0.251     0.903

 Chisq= 0.9  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -0.8254    0.4381   0.8897 -0.928 0.354

Likelihood ratio test=0.92  on 1 df, p=0.3378
n= 22, number of events= 8 


============================

TCGA-COAD TLR3 

[1] 53
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        4     2.57     0.793      1.65
strata=LOW  11        3     4.43     0.461      1.65

 Chisq= 1.6  on 1 degrees of freedom, p= 0.2 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z     p
strataLOW -1.3402    0.2618   1.1217 -1.195 0.232

Likelihood ratio test=1.78  on 1 df, p=0.1827
n= 22, number of events= 7 


============================

TCGA-COAD FAF1 

[1] 54
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        2     2.09   0.00399   0.00864
strata=LOW  11        2     1.91   0.00437   0.00864

 Chisq= 0  on 1 degrees of freedom, p= 0.9 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)     z     p
strataLOW 0.09451   1.09912  1.01707 0.093 0.926

Likelihood ratio test=0.01  on 1 df, p=0.926
n= 22, number of events= 4 


============================

TCGA-COAD JAK1 

[1] 55
Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        3      1.9     0.634      1.21
strata=LOW  11        2      3.1     0.389      1.21

 Chisq= 1.2  on 1 degrees of freedom, p= 0.3 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z   p
strataLOW -1.1978    0.3019   1.1551 -1.037 0.3

Likelihood ratio test=1.25  on 1 df, p=0.2629
n= 22, number of events= 5 


============================

Display the results only for genes where a significant difference in survival has been reported.

significant_genes

[1] "TNFRSF1A"

num_significant_genes <- length(significant_genes)

if (num_significant_genes > 0) {
  for (i in 1 : num_significant_genes) {
    project <- significant_projects[[i]]
    gene <- significant_genes[[i]]
    
    cat(project, gene, "\n\n")
    gene_df <- construct_gene_df(gene, project)
    
    fit <- compute_surival_fit(gene_df)
    survival <- compute_survival_diff(gene_df)
    cox <- compute_cox(gene_df)
    print(survival)
    cat("\n")
    print(cox)
    print(plot_survival(fit))
    
    cat("\n\n============================\n\n")
  } 
}

TCGA-COAD TNFRSF1A 

Call:
survdiff(formula = Surv(overall_survival, deceased) ~ strata, 
    data = gene_df)

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 11        5     2.64      2.10      4.04
strata=LOW  11        2     4.36      1.28      4.04

 Chisq= 4  on 1 degrees of freedom, p= 0.04 

Call:
coxph(formula = Surv(overall_survival, deceased) ~ strata, data = gene_df)

             coef exp(coef) se(coef)      z      p
strataLOW -1.9975    0.1357   1.1300 -1.768 0.0771

Likelihood ratio test=4.28  on 1 df, p=0.03856
n= 22, number of events= 7 


============================

---

1. De La Salle University, Manila, Philippines, gonzales.markedward@gmail.com↩︎

2. De La Salle University, Manila, Philippines, anish.shrestha@dlsu.edu.ph↩︎