Survival Analysis

Colorectal Cancer | Necroptosis | Unique Genes per RCD Type | Log Fold Change

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) {
  normal_df <- tcga_matrix[[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")))
  normal_df$case_id <- paste0(sapply(strsplit(as.character(normal_df$case_id), "-"), `[`, 1), '-',
                            sapply(strsplit(as.character(normal_df$case_id), "-"), `[`, 2), '-', 
                            sapply(strsplit(as.character(normal_df$case_id), "-"), `[`, 3))
  
  tumor_df <- tcga_matrix[[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")))
  tumor_df$case_id <- paste0(sapply(strsplit(as.character(tumor_df$case_id), "-"), `[`, 1), '-',
                            sapply(strsplit(as.character(tumor_df$case_id), "-"), `[`, 2), '-', 
                            sapply(strsplit(as.character(tumor_df$case_id), "-"), `[`, 3))
  
  gene_df <- inner_join(normal_df, tumor_df, by = c("gene_id", "case_id", "deathtype", "gene", "description", "gene_biotype", "pmid", "comment"))
  gene_df$log_fold = log2(gene_df$counts.y / gene_df$counts.x)
  
  gene_df$strata <- ifelse(abs(gene_df$log_fold) >= 1.5, "HIGH", "LOW")
  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  9        2     1.13    0.6795     0.769
strata=LOW  37       10    10.87    0.0703     0.769

 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 -0.6831    0.5051   0.7938 -0.86 0.39

Likelihood ratio test=0.64  on 1 df, p=0.4224
n= 46, number of events= 12 


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

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  7        2     1.23     0.482     0.562
strata=LOW  39       10    10.77     0.055     0.562

 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 -0.5963    0.5508   0.8068 -0.739 0.46

Likelihood ratio test=0.49  on 1 df, p=0.4849
n= 46, number of events= 12 


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

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 17        3     4.42     0.454     0.775
strata=LOW  29        9     7.58     0.265     0.775

 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 0.5917    1.8071   0.6819 0.868 0.385

Likelihood ratio test=0.81  on 1 df, p=0.3674
n= 46, number of events= 12 


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

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 21        5     5.24   0.01133    0.0219
strata=LOW  25        7     6.76   0.00879    0.0219

 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 0.09016   1.09434  0.60925 0.148 0.882

Likelihood ratio test=0.02  on 1 df, p=0.8822
n= 46, number of events= 12 


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

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 16        4     4.32    0.0241      0.04
strata=LOW  30        8     7.68    0.0136      0.04

 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 0.1257    1.1339   0.6289 0.2 0.842

Likelihood ratio test=0.04  on 1 df, p=0.8408
n= 46, number of events= 12 


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

TCGA-COAD IFNB1 

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

n=8, 38 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 7        2    2.607     0.141      1.19
strata=LOW  1        1    0.393     0.938      1.19

 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 1.445     4.243    1.435 1.007 0.314

Likelihood ratio test=0.94  on 1 df, p=0.3318
n= 8, number of events= 3 
   (38 observations deleted due to missingness)


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

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 14        3     3.86    0.1926     0.299
strata=LOW  32        9     8.14    0.0914     0.299

 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 0.3692    1.4466   0.6791 0.544 0.587

Likelihood ratio test=0.31  on 1 df, p=0.577
n= 46, number of events= 12 


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

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 16        4     3.69    0.0259    0.0413
strata=LOW  30        8     8.31    0.0115    0.0413

 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 -0.1314    0.8768   0.6473 -0.203 0.839

Likelihood ratio test=0.04  on 1 df, p=0.8398
n= 46, number of events= 12 


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

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  9        1     2.31     0.740     0.968
strata=LOW  37       11     9.69     0.176     0.968

 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 0.9971    2.7105   1.0558 0.944 0.345

Likelihood ratio test=1.16  on 1 df, p=0.282
n= 46, number of events= 12 


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

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 24        5     5.28    0.0148    0.0296
strata=LOW  22        7     6.72    0.0117    0.0296

 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 0.1059    1.1117   0.6159 0.172 0.864

Likelihood ratio test=0.03  on 1 df, p=0.8634
n= 46, number of events= 12 


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

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 23        6     6.23   0.00850    0.0199
strata=LOW  23        6     5.77   0.00918    0.0199

 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 0.08592   1.08972  0.60968 0.141 0.888

Likelihood ratio test=0.02  on 1 df, p=0.8881
n= 46, number of events= 12 


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

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 24        6     5.68    0.0179    0.0373
strata=LOW  22        6     6.32    0.0161    0.0373

 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 -0.1176    0.8891   0.6087 -0.193 0.847

Likelihood ratio test=0.04  on 1 df, p=0.8466
n= 46, number of events= 12 


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

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 14        3     4.18     0.331     0.615
strata=LOW  32        9     7.82     0.177     0.615

 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 0.6051    1.8314   0.7833 0.773 0.44

Likelihood ratio test=0.67  on 1 df, p=0.4119
n= 46, number of events= 12 


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

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        2     1.88   0.00754   0.00935
strata=LOW  35       10    10.12   0.00140   0.00935

 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 -0.07666   0.92621  0.79318 -0.097 0.923

Likelihood ratio test=0.01  on 1 df, p=0.9236
n= 46, number of events= 12 


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

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 29        6     7.12     0.175     0.514
strata=LOW  17        6     4.88     0.255     0.514

 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 0.4380    1.5496   0.6156 0.711 0.477

Likelihood ratio test=0.5  on 1 df, p=0.4814
n= 46, number of events= 12 


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

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  8        2     1.77   0.02966    0.0362
strata=LOW  38       10    10.23   0.00513    0.0362

 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 -0.1504    0.8603   0.7919 -0.19 0.849

Likelihood ratio test=0.04  on 1 df, p=0.8516
n= 46, number of events= 12 


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

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  9        2     1.75   0.03557    0.0434
strata=LOW  37       10    10.25   0.00607    0.0434

 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 -0.1649    0.8479   0.7930 -0.208 0.835

Likelihood ratio test=0.04  on 1 df, p=0.8379
n= 46, number of events= 12 


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

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 21        4     5.06     0.222     0.462
strata=LOW  25        8     6.94     0.162     0.462

 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 0.4401    1.5529   0.6525 0.674 0.5

Likelihood ratio test=0.46  on 1 df, p=0.4958
n= 46, number of events= 12 


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

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 25        6     6.59    0.0528     0.135
strata=LOW  21        6     5.41    0.0644     0.135

 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 0.2247    1.2519   0.6127 0.367 0.714

Likelihood ratio test=0.13  on 1 df, p=0.715
n= 46, number of events= 12 


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

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 24        5     5.98     0.160     0.357
strata=LOW  22        7     6.02     0.159     0.357

 Chisq= 0.4  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 0.3629    1.4375   0.6109 0.594 0.552

Likelihood ratio test=0.36  on 1 df, p=0.5513
n= 46, number of events= 12 


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

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  6        1     1.06  0.003552   0.00404
strata=LOW  40       11    10.94  0.000345   0.00404

 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 0.06758   1.06991  1.06283 0.064 0.949

Likelihood ratio test=0  on 1 df, p=0.9489
n= 46, number of events= 12 


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

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  9        2     1.13    0.6795     0.769
strata=LOW  37       10    10.87    0.0703     0.769

 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 -0.6831    0.5051   0.7938 -0.86 0.39

Likelihood ratio test=0.64  on 1 df, p=0.4224
n= 46, number of events= 12 


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

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  7        1     1.63    0.2436     0.302
strata=LOW  39       11    10.37    0.0383     0.302

 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 0.5781    1.7826   1.0660 0.542 0.588

Likelihood ratio test=0.34  on 1 df, p=0.5598
n= 46, number of events= 12 


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

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 18        4     3.95  0.000754   0.00126
strata=LOW  28        8     8.05  0.000369   0.00126

 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 -0.02297   0.97729  0.64838 -0.035 0.972

Likelihood ratio test=0  on 1 df, p=0.9718
n= 46, number of events= 12 


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

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 26        7     8.35     0.219     0.775
strata=LOW  20        5     3.65     0.503     0.775

 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 0.5374    1.7116   0.6173 0.871 0.384

Likelihood ratio test=0.74  on 1 df, p=0.3896
n= 46, number of events= 12 


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

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 13        3     2.35    0.1804     0.231
strata=LOW  33        9     9.65    0.0439     0.231

 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 -0.3257    0.7221   0.6802 -0.479 0.632

Likelihood ratio test=0.22  on 1 df, p=0.6407
n= 46, number of events= 12 


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

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        2     2.66    0.1633     0.225
strata=LOW  34       10     9.34    0.0465     0.225

 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 0.3742    1.4538   0.7938 0.471 0.637

Likelihood ratio test=0.24  on 1 df, p=0.6253
n= 46, number of events= 12 


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

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  9        2     1.61    0.0941     0.113
strata=LOW  37       10    10.39    0.0146     0.113

 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 -0.2651    0.7671   0.7924 -0.335 0.738

Likelihood ratio test=0.11  on 1 df, p=0.7446
n= 46, number of events= 12 


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

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 13        3     3.18   0.01069    0.0157
strata=LOW  33        9     8.82   0.00386    0.0157

 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 0.08662   1.09048  0.69088 0.125 0.9

Likelihood ratio test=0.02  on 1 df, p=0.8997
n= 46, number of events= 12 


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

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 14        2     3.41     0.585      0.86
strata=LOW  32       10     8.59     0.232      0.86

 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 0.7132    2.0406   0.7853 0.908 0.364

Likelihood ratio test=0.95  on 1 df, p=0.3303
n= 46, number of events= 12 


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

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 18        4      3.9   0.00256   0.00423
strata=LOW  28        8      8.1   0.00123   0.00423

 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 -0.04215   0.95873  0.64791 -0.065 0.948

Likelihood ratio test=0  on 1 df, p=0.9482
n= 46, number of events= 12 


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

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 19        5     3.72     0.442     0.723
strata=LOW  27        7     8.28     0.198     0.723

 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 -0.5413    0.5820   0.6441 -0.84 0.401

Likelihood ratio test=0.7  on 1 df, p=0.4026
n= 46, number of events= 12 


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

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  8        2     1.67    0.0645    0.0777
strata=LOW  38       10    10.33    0.0104    0.0777

 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 -0.2202    0.8023   0.7920 -0.278 0.781

Likelihood ratio test=0.07  on 1 df, p=0.7856
n= 46, number of events= 12 


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

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 17        4     3.85   0.00622    0.0097
strata=LOW  29        8     8.15   0.00293    0.0097

 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 -0.06218   0.93971  0.63142 -0.098 0.922

Likelihood ratio test=0.01  on 1 df, p=0.9218
n= 46, number of events= 12 


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

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 18        2     4.07     1.056      1.85
strata=LOW  28       10     7.93     0.543      1.85

 Chisq= 1.8  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 1.037     2.821    0.797 1.301 0.193

Likelihood ratio test=2.01  on 1 df, p=0.156
n= 46, number of events= 12 


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

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 13        3     3.28   0.02335    0.0336
strata=LOW  33        9     8.72   0.00877    0.0336

 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 0.1245    1.1326   0.6800 0.183 0.855

Likelihood ratio test=0.03  on 1 df, p=0.8534
n= 46, number of events= 12 


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

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 26        8     5.42      1.23      2.52
strata=LOW  20        4     6.58      1.01      2.52

 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 -1.0601    0.3464   0.6953 -1.525 0.127

Likelihood ratio test=2.62  on 1 df, p=0.1053
n= 46, number of events= 12 


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

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 16        4     3.86   0.00543   0.00844
strata=LOW  30        8     8.14   0.00257   0.00844

 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 -0.05788   0.94376  0.62996 -0.092 0.927

Likelihood ratio test=0.01  on 1 df, p=0.927
n= 46, number of events= 12 


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

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  7        1     2.05     0.539     0.681
strata=LOW  39       11     9.95     0.111     0.681

 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 0.8459    2.3300   1.0555 0.801 0.423

Likelihood ratio test=0.8  on 1 df, p=0.3704
n= 46, number of events= 12 


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

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 16        4     3.65    0.0345    0.0546
strata=LOW  30        8     8.35    0.0151    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 -0.1510    0.8598   0.6471 -0.233 0.815

Likelihood ratio test=0.05  on 1 df, p=0.8165
n= 46, number of events= 12 


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

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 20        4     4.41    0.0380    0.0692
strata=LOW  26        8     7.59    0.0221    0.0692

 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 0.1709    1.1864   0.6507 0.263 0.793

Likelihood ratio test=0.07  on 1 df, p=0.7918
n= 46, number of events= 12 


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

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  9        2      2.2    0.0179     0.023
strata=LOW  37       10      9.8    0.0040     0.023

 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 0.1202    1.1278   0.7928 0.152 0.879

Likelihood ratio test=0.02  on 1 df, p=0.8781
n= 46, number of events= 12 


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

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 32        8     7.01     0.141     0.392
strata=LOW  14        4     4.99     0.198     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 -0.4219    0.6558   0.6790 -0.621 0.534

Likelihood ratio test=0.41  on 1 df, p=0.5225
n= 46, number of events= 12 


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

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 10        3     1.86     0.697     0.863
strata=LOW  36        9    10.14     0.128     0.863

 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 -0.6345    0.5302   0.6942 -0.914 0.361

Likelihood ratio test=0.76  on 1 df, p=0.3829
n= 46, number of events= 12 


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

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 21        5      4.9   0.00192   0.00357
strata=LOW  25        7      7.1   0.00133   0.00357

 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 -0.03668   0.96399  0.61408 -0.06 0.952

Likelihood ratio test=0  on 1 df, p=0.9524
n= 46, number of events= 12 


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

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  9        1     2.52     0.913      1.23
strata=LOW  37       11     9.48     0.242      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 1.113     3.044    1.056 1.054 0.292

Likelihood ratio test=1.48  on 1 df, p=0.2232
n= 46, number of events= 12 


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

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  8        1     2.16     0.622     0.804
strata=LOW  38       11     9.84     0.137     0.804

 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 0.9162    2.4997   1.0576 0.866 0.386

Likelihood ratio test=0.95  on 1 df, p=0.3291
n= 46, number of events= 12 


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

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 16        3     3.51    0.0729     0.115
strata=LOW  30        9     8.49    0.0301     0.115

 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 0.2363    1.2666   0.6978 0.339 0.735

Likelihood ratio test=0.12  on 1 df, p=0.7313
n= 46, number of events= 12 


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

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  6        1     1.73    0.3108     0.377
strata=LOW  40       11    10.27    0.0525     0.377

 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 0.6365    1.8899   1.0547 0.604 0.546

Likelihood ratio test=0.43  on 1 df, p=0.5114
n= 46, number of events= 12 


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

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 17        4     3.65    0.0345    0.0546
strata=LOW  29        8     8.35    0.0151    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 -0.1510    0.8598   0.6471 -0.233 0.815

Likelihood ratio test=0.05  on 1 df, p=0.8165
n= 46, number of events= 12 


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

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        2     2.56    0.1228     0.167
strata=LOW  34       10     9.44    0.0333     0.167

 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 0.3228    1.3810   0.7939 0.407 0.684

Likelihood ratio test=0.18  on 1 df, p=0.675
n= 46, number of events= 12 


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

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 17        5     4.07     0.212     0.363
strata=LOW  29        7     7.93     0.109     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 -0.3811    0.6831   0.6367 -0.599 0.549

Likelihood ratio test=0.36  on 1 df, p=0.5506
n= 46, number of events= 12 


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

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 33        7      8.4     0.234      1.01
strata=LOW  13        5      3.6     0.545      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 0.6268    1.8717   0.6339 0.989 0.323

Likelihood ratio test=0.91  on 1 df, p=0.3391
n= 46, number of events= 12 


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

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 10        2     2.24   0.02644    0.0343
strata=LOW  36       10     9.76   0.00608    0.0343

 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 0.1469    1.1582   0.7933 0.185 0.853

Likelihood ratio test=0.04  on 1 df, p=0.851
n= 46, number of events= 12 


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

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 19        5     3.98     0.260     0.436
strata=LOW  27        7     8.02     0.129     0.436

 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 -0.4163    0.6595   0.6351 -0.655 0.512

Likelihood ratio test=0.43  on 1 df, p=0.5136
n= 46, number of events= 12 


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

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

significant_genes

NULL

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)
    
    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")
  } 
}

---

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

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