Survival Analysis

Colorectal Cancer | Necroptosis | Unique Genes per RCD Type | Gene Expression of Tumor 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 == "tumor")))

  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 12        5     3.46     0.682      1.69
strata=LOW  12        2     3.54     0.668      1.69

 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 -1.0980    0.3335   0.8831 -1.243 0.214

Likelihood ratio test=1.66  on 1 df, p=0.1972
n= 24, number of events= 7 


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

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 12        3     2.56    0.0753     0.158
strata=LOW  12        2     2.44    0.0790     0.158

 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 -0.3653    0.6940   0.9231 -0.396 0.692

Likelihood ratio test=0.16  on 1 df, p=0.6897
n= 24, number of events= 5 


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

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 12        2      2.8     0.230     0.665
strata=LOW  12        3      2.2     0.294     0.665

 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 0.9168    2.5013   1.1622 0.789 0.43

Likelihood ratio test=0.7  on 1 df, p=0.4015
n= 24, number of events= 5 


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

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 12        5     5.63    0.0703     0.198
strata=LOW  12        4     3.37    0.1174     0.198

 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 0.3068    1.3591   0.6916 0.444 0.657

Likelihood ratio test=0.19  on 1 df, p=0.6592
n= 24, number of events= 9 


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

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 12        2     2.23    0.0246    0.0567
strata=LOW  12        3     2.77    0.0199    0.0567

 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 0.2397    1.2709   1.0088 0.238 0.812

Likelihood ratio test=0.06  on 1 df, p=0.8124
n= 24, number of events= 5 


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

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 12        2     2.23    0.0246    0.0567
strata=LOW  12        3     2.77    0.0199    0.0567

 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 0.2397    1.2709   1.0088 0.238 0.812

Likelihood ratio test=0.06  on 1 df, p=0.8124
n= 24, number of events= 5 


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

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 12        3     3.31    0.0295    0.0572
strata=LOW  12        5     4.69    0.0209    0.0572

 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 0.1843    1.2023   0.7712 0.239 0.811

Likelihood ratio test=0.06  on 1 df, p=0.8105
n= 24, 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 12        2     1.68    0.0614      0.14
strata=LOW  12        3     3.32    0.0310      0.14

 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 -0.4567    0.6333   1.2290 -0.372 0.71

Likelihood ratio test=0.14  on 1 df, p=0.7042
n= 24, number of events= 5 


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

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 12        4     4.05   0.00073   0.00301
strata=LOW  12        2     1.95   0.00152   0.00301

 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 0.05532   1.05688  1.00841 0.055 0.956

Likelihood ratio test=0  on 1 df, p=0.9563
n= 24, number of events= 6 


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

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 12        5     3.51     0.633      1.27
strata=LOW  12        2     3.49     0.637      1.27

 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 -0.9122    0.4016   0.8371 -1.09 0.276

Likelihood ratio test=1.31  on 1 df, p=0.2516
n= 24, 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 12        3     2.47    0.1134     0.224
strata=LOW  12        3     3.53    0.0794     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 -0.4292    0.6510   0.9131 -0.47 0.638

Likelihood ratio test=0.23  on 1 df, p=0.6347
n= 24, number of events= 6 


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

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 12        4     4.97     0.190     0.565
strata=LOW  12        4     3.03     0.312     0.565

 Chisq= 0.6  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 0.5737    1.7748   0.7730 0.742 0.458

Likelihood ratio test=0.56  on 1 df, p=0.4552
n= 24, number of events= 8 


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

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 12        3     4.08     0.285     0.776
strata=LOW  12        4     2.92     0.397     0.776

 Chisq= 0.8  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 0.7463    2.1091   0.8668 0.861 0.389

Likelihood ratio test=0.79  on 1 df, p=0.3746
n= 24, number of events= 7 


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

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 12        5     4.94  0.000797   0.00242
strata=LOW  12        3     3.06  0.001285   0.00242

 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 -0.03864   0.96210  0.78546 -0.049 0.961

Likelihood ratio test=0  on 1 df, p=0.9607
n= 24, 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 12        2      2.2    0.0182    0.0412
strata=LOW  12        2      1.8    0.0222    0.0412

 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 0.2047    1.2271   1.0101 0.203 0.839

Likelihood ratio test=0.04  on 1 df, p=0.8395
n= 24, number of events= 4 


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

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 12        3     2.32     0.202     0.635
strata=LOW  12        1     1.68     0.278     0.635

 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 -0.9410    0.3902   1.2251 -0.768 0.442

Likelihood ratio test=0.63  on 1 df, p=0.4262
n= 24, number of events= 4 


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

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 12        3     3.27    0.0219    0.0483
strata=LOW  12        4     3.73    0.0192    0.0483

 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 0.1794    1.1965   0.8177 0.219 0.826

Likelihood ratio test=0.05  on 1 df, p=0.8265
n= 24, number of events= 7 


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

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 12        4     2.31     1.239      2.31
strata=LOW  12        2     3.69     0.775      2.31

 Chisq= 2.3  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 -1.5455    0.2132   1.1195 -1.381 0.167

Likelihood ratio test=2.43  on 1 df, p=0.1193
n= 24, number of events= 6 


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

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 12        2     2.99     0.329     0.871
strata=LOW  12        3     2.01     0.491     0.871

 Chisq= 0.9  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 0.8613    2.3663   0.9462 0.91 0.363

Likelihood ratio test=0.85  on 1 df, p=0.3556
n= 24, number of events= 5 


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

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 12        3      3.8     0.169     0.326
strata=LOW  12        5      4.2     0.153     0.326

 Chisq= 0.3  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 0.4163    1.5164   0.7343 0.567 0.571

Likelihood ratio test=0.33  on 1 df, p=0.5655
n= 24, number of events= 8 


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

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 12        4     4.07   0.00108    0.0027
strata=LOW  12        3     2.93   0.00150    0.0027

 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 0.04065   1.04149  0.78216 0.052 0.959

Likelihood ratio test=0  on 1 df, p=0.9586
n= 24, 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 12        5     3.46     0.682      1.69
strata=LOW  12        2     3.54     0.668      1.69

 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 -1.0980    0.3335   0.8831 -1.243 0.214

Likelihood ratio test=1.66  on 1 df, p=0.1972
n= 24, number of events= 7 


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

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 12        1    0.971  0.000891   0.00173
strata=LOW  12        2    2.029  0.000426   0.00173

 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 -0.05889   0.94281  1.41483 -0.042 0.967

Likelihood ratio test=0  on 1 df, p=0.9668
n= 24, number of events= 3 


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

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 12        1     2.73      1.09      2.53
strata=LOW  12        4     2.27      1.31      2.53

 Chisq= 2.5  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 1.627     5.090    1.131 1.439 0.15

Likelihood ratio test=2.62  on 1 df, p=0.1054
n= 24, number of events= 5 


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

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 12        5     4.86   0.00429    0.0127
strata=LOW  12        3     3.14   0.00662    0.0127

 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 -0.08847   0.91533  0.78465 -0.113 0.91

Likelihood ratio test=0.01  on 1 df, p=0.9101
n= 24, number of events= 8 


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

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 12        2     1.74    0.0399     0.063
strata=LOW  12        3     3.26    0.0212     0.063

 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 -0.2325    0.7925   0.9281 -0.251 0.802

Likelihood ratio test=0.06  on 1 df, p=0.8037
n= 24, number of events= 5 


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

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 12        1     2.46     0.865      1.72
strata=LOW  12        5     3.54     0.600      1.72

 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 1.365     3.917    1.122 1.217 0.224

Likelihood ratio test=1.85  on 1 df, p=0.1741
n= 24, number of events= 6 


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

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 12        4     2.53     0.848      1.54
strata=LOW  12        3     4.47     0.481      1.54

 Chisq= 1.5  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 -1.0651    0.3447   0.8931 -1.193 0.233

Likelihood ratio test=1.53  on 1 df, p=0.2156
n= 24, number of events= 7 


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

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 12        2     2.31    0.0428    0.0836
strata=LOW  12        4     3.69    0.0269    0.0836

 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 0.2688    1.3084   0.9323 0.288 0.773

Likelihood ratio test=0.08  on 1 df, p=0.7717
n= 24, 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 12        0     2.17      2.17      4.75
strata=LOW  12        4     1.83      2.56      4.75

 Chisq= 4.8  on 1 degrees of freedom, p= 0.03 

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

               coef exp(coef)  se(coef)     z     p
strataLOW 2.140e+01 1.959e+09 2.025e+04 0.001 0.999

Likelihood ratio test=6.27  on 1 df, p=0.0123
n= 24, number of events= 4 


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

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 12        2     2.67     0.169     0.363
strata=LOW  12        4     3.33     0.135     0.363

 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 0.5438    1.7226   0.9138 0.595 0.552

Likelihood ratio test=0.36  on 1 df, p=0.5469
n= 24, number of events= 6 


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

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 12        2     1.42     0.242     0.459
strata=LOW  12        1     1.58     0.216     0.459

 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 -0.8082    0.4457   1.2259 -0.659 0.51

Likelihood ratio test=0.46  on 1 df, p=0.4959
n= 24, number of events= 3 


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

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 12        1     1.88     0.411     0.832
strata=LOW  12        3     2.12     0.364     0.832

 Chisq= 0.8  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 1.033     2.811    1.178 0.877 0.38

Likelihood ratio test=0.87  on 1 df, p=0.3509
n= 24, number of events= 4 


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

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 12        1     1.48    0.1584      0.32
strata=LOW  12        3     2.52    0.0935      0.32

 Chisq= 0.3  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 0.6849    1.9836   1.2344 0.555 0.579

Likelihood ratio test=0.33  on 1 df, p=0.5681
n= 24, number of events= 4 


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

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 12        4     4.03  0.000172  0.000693
strata=LOW  12        2     1.97  0.000351  0.000693

 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 0.02633   1.02668  1.00026 0.026 0.979

Likelihood ratio test=0  on 1 df, p=0.979
n= 24, 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 12        3      3.8     0.170     0.673
strata=LOW  12        3      2.2     0.294     0.673

 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 0.9297    2.5339   1.1708 0.794 0.427

Likelihood ratio test=0.71  on 1 df, p=0.3984
n= 24, 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 12        3      3.5    0.0720     0.149
strata=LOW  12        5      4.5    0.0561     0.149

 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 0.2975    1.3465   0.7745 0.384 0.701

Likelihood ratio test=0.15  on 1 df, p=0.6995
n= 24, number of events= 8 


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

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 12        0    0.444     0.444       0.8
strata=LOW  12        2    1.556     0.127       0.8

 Chisq= 0.8  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 2.100e+01 1.317e+09 4.057e+04 0.001 1

Likelihood ratio test=1.18  on 1 df, p=0.2783
n= 24, number of events= 2 


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

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 12        3     2.87   0.00606    0.0175
strata=LOW  12        2     2.13   0.00816    0.0175

 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 -0.1323    0.8761   1.0010 -0.132 0.895

Likelihood ratio test=0.02  on 1 df, p=0.8949
n= 24, number of events= 5 


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

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 12        2     2.66     0.165     0.611
strata=LOW  12        2     1.34     0.329     0.611

 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 0.9385    2.5560   1.2414 0.756 0.45

Likelihood ratio test=0.61  on 1 df, p=0.4341
n= 24, number of events= 4 


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

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 12        3     4.32     0.403      1.58
strata=LOW  12        3     1.68     1.036      1.58

 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 1.0994    3.0023   0.9186 1.197 0.231

Likelihood ratio test=1.47  on 1 df, p=0.2257
n= 24, number of events= 6 


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

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 12        3     4.83     0.694      2.25
strata=LOW  12        5     3.17     1.059      2.25

 Chisq= 2.2  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 1.499     4.479    1.096 1.368 0.171

Likelihood ratio test=2.48  on 1 df, p=0.1149
n= 24, number of events= 8 


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

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 12        2     2.65     0.161     0.344
strata=LOW  12        4     3.35     0.128     0.344

 Chisq= 0.3  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 0.5298    1.6986   0.9135 0.58 0.562

Likelihood ratio test=0.34  on 1 df, p=0.5573
n= 24, number of events= 6 


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

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 12        1        3      1.33         4
strata=LOW  12        4        2      2.00         4

 Chisq= 4  on 1 degrees of freedom, p= 0.05 

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

               coef exp(coef)  se(coef)     z     p
strataLOW 2.120e+01 1.615e+09 2.010e+04 0.001 0.999

Likelihood ratio test=5.55  on 1 df, p=0.01853
n= 24, number of events= 5 


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

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 12        2     3.09     0.383     0.793
strata=LOW  12        5     3.91     0.302     0.793

 Chisq= 0.8  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 0.7554    2.1284   0.8681 0.87 0.384

Likelihood ratio test=0.81  on 1 df, p=0.3694
n= 24, number of events= 7 


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

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 12        3     3.29    0.0259    0.0581
strata=LOW  12        3     2.71    0.0315    0.0581

 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 0.1979    1.2188   0.8221 0.241 0.81

Likelihood ratio test=0.06  on 1 df, p=0.8099
n= 24, 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 12        4     3.18     0.211       0.4
strata=LOW  12        3     3.82     0.176       0.4

 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 -0.4853    0.6155   0.7744 -0.627 0.531

Likelihood ratio test=0.4  on 1 df, p=0.5285
n= 24, number of events= 7 


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

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 12        3     3.52    0.0765     0.187
strata=LOW  12        4     3.48    0.0773     0.187

 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 0.3524    1.4225   0.8201 0.43 0.667

Likelihood ratio test=0.18  on 1 df, p=0.6682
n= 24, 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 12        4     2.45     0.979      1.96
strata=LOW  12        1     2.55     0.941      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 -1.4467    0.2354   1.1235 -1.288 0.198

Likelihood ratio test=2.08  on 1 df, p=0.1492
n= 24, number of events= 5 


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

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 12        2     1.43     0.231     0.445
strata=LOW  12        2     2.57     0.128     0.445

 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 -0.8009    0.4489   1.2316 -0.65 0.515

Likelihood ratio test=0.45  on 1 df, p=0.502
n= 24, number of events= 4 


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

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 12        4      3.2     0.201     0.666
strata=LOW  12        1      1.8     0.357     0.666

 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 -0.9194    0.3988   1.1646 -0.789 0.43

Likelihood ratio test=0.7  on 1 df, p=0.4012
n= 24, number of events= 5 


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

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 12        0     1.63     1.630      2.82
strata=LOW  12        5     3.37     0.789      2.82

 Chisq= 2.8  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 2.094e+01 1.239e+09 2.079e+04 0.001 0.999

Likelihood ratio test=4.26  on 1 df, p=0.0391
n= 24, number of events= 5 


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

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 12        2     2.08   0.00342   0.00608
strata=LOW  12        3     2.92   0.00244   0.00608

 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 0.07243   1.07512  0.92922 0.078 0.938

Likelihood ratio test=0.01  on 1 df, p=0.9377
n= 24, number of events= 5 


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

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 12        4     3.91   0.00221   0.00514
strata=LOW  12        4     4.09   0.00211   0.00514

 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 -0.05552   0.94599  0.77455 -0.072 0.943

Likelihood ratio test=0.01  on 1 df, p=0.9428
n= 24, number of events= 8 


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

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 12        2     4.21      1.16      3.27
strata=LOW  12        5     2.79      1.74      3.27

 Chisq= 3.3  on 1 degrees of freedom, p= 0.07 

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

           coef exp(coef) se(coef)     z    p
strataLOW 1.754     5.780    1.097 1.599 0.11

Likelihood ratio test=3.51  on 1 df, p=0.06118
n= 24, number of events= 7 


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

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

significant_genes

[1] "EIF2AK2" "VDAC1"  

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 EIF2AK2 

Warning: Loglik converged before variable  1 ; coefficient may be infinite. 

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

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 12        0     2.17      2.17      4.75
strata=LOW  12        4     1.83      2.56      4.75

 Chisq= 4.8  on 1 degrees of freedom, p= 0.03 

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

               coef exp(coef)  se(coef)     z     p
strataLOW 2.140e+01 1.959e+09 2.025e+04 0.001 0.999

Likelihood ratio test=6.27  on 1 df, p=0.0123
n= 24, number of events= 4 


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

TCGA-COAD VDAC1 

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

             N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 12        1        3      1.33         4
strata=LOW  12        4        2      2.00         4

 Chisq= 4  on 1 degrees of freedom, p= 0.05 

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

               coef exp(coef)  se(coef)     z     p
strataLOW 2.120e+01 1.615e+09 2.010e+04 0.001 0.999

Likelihood ratio test=5.55  on 1 df, p=0.01853
n= 24, number of events= 5 


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

---

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

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