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.

GDC_DIR = "../data/public/GDCdata"

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 code block below

  # GDCdownload(
  #   query_coad,
  #   directory = GDC_DIR
  # )

  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]], 
    directory = GDC_DIR,
    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 <- "../data/public/rcd-gene-list/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")
    cat(project, gene, "\n\n")
    error <- tryCatch (
      {
        gene_df <- construct_gene_df(gene, project)
      },
      error = function(e) {
        cat("\n\n============================\n\n")
        e
      }
    )
    
    if(inherits(error, "error")) next

    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 

TCGA-COAD MLKL 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 5        5     4.31     0.109     0.339
strata=LOW  2        2     2.69     0.175     0.339

 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 -0.5152    0.5974   0.8935 -0.577 0.564

Likelihood ratio test=0.35  on 1 df, p=0.5557
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD RBCK1 

TCGA-COAD RBCK1 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     4.35     0.419      4.26
strata=LOW  2        2     0.65     2.804      4.26

 Chisq= 4.3  on 1 degrees of freedom, p= 0.04 

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

               coef exp(coef)  se(coef)     z     p
strataLOW 2.232e+01 4.923e+09 3.308e+04 0.001 0.999

Likelihood ratio test=4.61  on 1 df, p=0.03188
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD JAK2 

TCGA-COAD JAK2 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     2.48    0.0941      0.26
strata=LOW  3        3     2.52    0.0928      0.26

 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 0.5892    1.8026   1.1718 0.503 0.615

Likelihood ratio test=0.27  on 1 df, p=0.6001
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD ZBP1 

TCGA-COAD ZBP1 

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

n=9, 15 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 5        5     4.19     0.155     0.333
strata=LOW  4        4     4.81     0.135     0.333

 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 -0.4224    0.6555   0.7374 -0.573 0.567

Likelihood ratio test=0.34  on 1 df, p=0.5615
n= 9, number of events= 9 
   (15 observations deleted due to missingness)


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

TCGA-COAD RNF31 

TCGA-COAD RNF31 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     1.73    0.0410    0.0739
strata=LOW  3        3     3.27    0.0218    0.0739

 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 -0.2739    0.7604   1.0107 -0.271 0.786

Likelihood ratio test=0.07  on 1 df, p=0.7866
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD IFNB1 

TCGA-COAD IFNB1 



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

TCGA-COAD TRAF5 

TCGA-COAD TRAF5 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     1.03     3.788      5.28
strata=LOW  5        5     6.97     0.558      5.28

 Chisq= 5.3  on 1 degrees of freedom, p= 0.02 

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

             coef exp(coef) se(coef)      z      p
strataLOW -2.2495    0.1055   1.1729 -1.918 0.0551

Likelihood ratio test=4.43  on 1 df, p=0.03535
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD BIRC2 

TCGA-COAD BIRC2 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2    0.983     1.051      1.59
strata=LOW  3        3    4.017     0.257      1.59

 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 -1.4388    0.2372   1.2359 -1.164 0.244

Likelihood ratio test=1.46  on 1 df, p=0.2262
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD TRAF2 

TCGA-COAD TRAF2 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4    5.467     0.393      5.63
strata=LOW  2        2    0.533     4.033      5.63

 Chisq= 5.6  on 1 degrees of freedom, p= 0.02 

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

               coef exp(coef)  se(coef)     z     p
strataLOW 2.195e+01 3.399e+09 2.380e+04 0.001 0.999

Likelihood ratio test=5.42  on 1 df, p=0.01995
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD BCL2 

TCGA-COAD BCL2 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 5        5     5.15   0.00423    0.0191
strata=LOW  2        2     1.85   0.01176    0.0191

 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 0.1278    1.1363   0.9241 0.138 0.89

Likelihood ratio test=0.02  on 1 df, p=0.8905
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD STAT4 

TCGA-COAD STAT4 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     1.98     0.521     0.899
strata=LOW  3        3     4.02     0.257     0.899

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

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

             coef exp(coef) se(coef)      z     p
strataLOW -0.8606    0.4229   0.9325 -0.923 0.356

Likelihood ratio test=0.87  on 1 df, p=0.3496
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD BIRC3 

TCGA-COAD BIRC3 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4      4.3    0.0216    0.0544
strata=LOW  4        4      3.7    0.0251    0.0544

 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 0.1798    1.1970   0.7718 0.233 0.816

Likelihood ratio test=0.05  on 1 df, p=0.8152
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD STAT1 

TCGA-COAD STAT1 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3        4     0.248      0.68
strata=LOW  4        4        3     0.330      0.68

 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 0.7106    2.0353   0.8786 0.809 0.419

Likelihood ratio test=0.69  on 1 df, p=0.405
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD STAT2 

TCGA-COAD STAT2 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 5        5     6.05     0.181     0.947
strata=LOW  3        3     1.95     0.561     0.947

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

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

            coef exp(coef) se(coef)     z     p
strataLOW 0.8766    2.4026   0.9275 0.945 0.345

Likelihood ratio test=0.92  on 1 df, p=0.338
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD TNFSF10 

TCGA-COAD TNFSF10 

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

n=4, 20 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     2.67     0.167     0.615
strata=LOW  2        2     1.33     0.333     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 0.9406    2.5616   1.2403 0.758 0.448

Likelihood ratio test=0.62  on 1 df, p=0.4325
n= 4, number of events= 4 
   (20 observations deleted due to missingness)


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

TCGA-COAD TYK2 

TCGA-COAD TYK2 

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

n=4, 20 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3    3.417    0.0508     0.424
strata=LOW  1        1    0.583    0.2976     0.424

 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 0.8959    2.4495   1.4215 0.63 0.529

Likelihood ratio test=0.38  on 1 df, p=0.535
n= 4, number of events= 4 
   (20 observations deleted due to missingness)


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

TCGA-COAD PPIA 

TCGA-COAD PPIA 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     3.45    0.0575     0.142
strata=LOW  4        4     3.55    0.0558     0.142

 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 0.3150    1.3703   0.8385 0.376 0.707

Likelihood ratio test=0.14  on 1 df, p=0.7076
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD TNFRSF1A 

TCGA-COAD TNFRSF1A 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     2.93     0.388     0.961
strata=LOW  2        2     3.07     0.371     0.961

 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 -1.0569    0.3475   1.1267 -0.938 0.348

Likelihood ratio test=1.05  on 1 df, p=0.3051
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD CAPN2 

TCGA-COAD CAPN2 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     1.73    0.0410    0.0739
strata=LOW  3        3     3.27    0.0218    0.0739

 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 -0.2739    0.7604   1.0107 -0.271 0.786

Likelihood ratio test=0.07  on 1 df, p=0.7866
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD FAS 

TCGA-COAD FAS 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     1.78     0.841      1.21
strata=LOW  5        5     6.22     0.240      1.21

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

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

             coef exp(coef) se(coef)      z     p
strataLOW -0.8821    0.4139   0.8251 -1.069 0.285

Likelihood ratio test=1.11  on 1 df, p=0.2921
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD PGAM5 

TCGA-COAD PGAM5 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     5.79     0.552      3.92
strata=LOW  3        3     1.21     2.638      3.92

 Chisq= 3.9  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 2.008     7.449    1.172 1.713 0.0867

Likelihood ratio test=3.53  on 1 df, p=0.06043
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD MLKL 

TCGA-COAD MLKL 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 5        5     4.31     0.109     0.339
strata=LOW  2        2     2.69     0.175     0.339

 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 -0.5152    0.5974   0.8935 -0.577 0.564

Likelihood ratio test=0.35  on 1 df, p=0.5557
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD FADD 

TCGA-COAD FADD 

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

n=3, 21 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 1        1    0.833    0.0333    0.0588
strata=LOW  2        2    2.167    0.0128    0.0588

 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 -0.3466    0.7071   1.4355 -0.241 0.809

Likelihood ratio test=0.06  on 1 df, p=0.8096
n= 3, number of events= 3 
   (21 observations deleted due to missingness)


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

TCGA-COAD TRPM7 

TCGA-COAD TRPM7 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 1        1     1.28    0.0626    0.0979
strata=LOW  4        4     3.72    0.0216    0.0979

 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 0.3695    1.4470   1.1865 0.311 0.755

Likelihood ratio test=0.1  on 1 df, p=0.7492
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD FASLG 

TCGA-COAD FASLG 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 5        5     6.22     0.240      1.21
strata=LOW  3        3     1.78     0.841      1.21

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

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

            coef exp(coef) se(coef)     z     p
strataLOW 0.8821    2.4159   0.8251 1.069 0.285

Likelihood ratio test=1.11  on 1 df, p=0.2921
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD TNFRSF10B 

TCGA-COAD TNFRSF10B 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     1.73    0.0410    0.0739
strata=LOW  3        3     3.27    0.0218    0.0739

 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 -0.2739    0.7604   1.0107 -0.271 0.786

Likelihood ratio test=0.07  on 1 df, p=0.7866
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD VPS4A 

TCGA-COAD VPS4A 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 1        1     0.95  0.002632   0.00353
strata=LOW  5        5     5.05  0.000495   0.00353

 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 -0.06936   0.93299  1.16790 -0.059 0.953

Likelihood ratio test=0  on 1 df, p=0.9529
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD TNFRSF10A 

TCGA-COAD TNFRSF10A 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     2.67     0.661       1.5
strata=LOW  3        3     4.33     0.408       1.5

 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 -1.3134    0.2689   1.1415 -1.151 0.25

Likelihood ratio test=1.62  on 1 df, p=0.2028
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD GLUD1 

TCGA-COAD GLUD1 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     2.07   0.00215   0.00375
strata=LOW  4        4     3.93   0.00113   0.00375

 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 0.05656   1.05819  0.92349 0.061 0.951

Likelihood ratio test=0  on 1 df, p=0.9511
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD EIF2AK2 

TCGA-COAD EIF2AK2 



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

TCGA-COAD CYLD 

TCGA-COAD CYLD 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2    0.783     1.890       2.6
strata=LOW  4        4    5.217     0.284       2.6

 Chisq= 2.6  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 -1.766     0.171    1.235 -1.43 0.153

Likelihood ratio test=2.2  on 1 df, p=0.1382
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD SPATA2 

TCGA-COAD SPATA2 

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

n=3, 21 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2    2.667     0.167         2
strata=LOW  1        1    0.333     1.333         2

 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 2.215e+01 4.171e+09 4.566e+04 0 1

Likelihood ratio test=2.2  on 1 df, p=0.1383
n= 3, number of events= 3 
   (21 observations deleted due to missingness)


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

TCGA-COAD DNM1L 

TCGA-COAD DNM1L 

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

n=4, 20 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 1        1     2.08     0.563      1.78
strata=LOW  3        3     1.92     0.612      1.78

 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 2.085e+01 1.136e+09 2.490e+04 0.001 0.999

Likelihood ratio test=2.77  on 1 df, p=0.09589
n= 4, number of events= 4 
   (20 observations deleted due to missingness)


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

TCGA-COAD CFLAR 

TCGA-COAD CFLAR 

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

n=4, 20 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 1        1     0.25      2.25         3
strata=LOW  3        3     3.75      0.15         3

 Chisq= 3  on 1 degrees of freedom, p= 0.08 

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

                coef  exp(coef)   se(coef)      z p
strataLOW -2.208e+01  2.562e-10  3.607e+04 -0.001 1

Likelihood ratio test=2.77  on 1 df, p=0.09589
n= 4, number of events= 4 
   (20 observations deleted due to missingness)


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

TCGA-COAD TICAM1 

TCGA-COAD TICAM1 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     4.88     0.160     0.985
strata=LOW  2        2     1.12     0.699     0.985

 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 0.9671    2.6303   1.0107 0.957 0.339

Likelihood ratio test=0.88  on 1 df, p=0.3471
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD HSP90AA1 

TCGA-COAD HSP90AA1 

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

n=6, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     4.52     0.509      2.56
strata=LOW  3        3     1.48     1.551      2.56

 Chisq= 2.6  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 1.688     5.408    1.172 1.44 0.15

Likelihood ratio test=2.47  on 1 df, p=0.1159
n= 6, number of events= 6 
   (19 observations deleted due to missingness)


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

TCGA-COAD IL33 

TCGA-COAD IL33 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     1.34     2.069      3.21
strata=LOW  5        5     6.66     0.415      3.21

 Chisq= 3.2  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 -1.8587    0.1559   1.1753 -1.581 0.114

Likelihood ratio test=3  on 1 df, p=0.08308
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD IRF9 

TCGA-COAD IRF9 



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

TCGA-COAD SHARPIN 

TCGA-COAD SHARPIN 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     3.52    0.0759     0.297
strata=LOW  2        2     1.48    0.1800     0.297

 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 0.5493    1.7321   1.0198 0.539 0.59

Likelihood ratio test=0.29  on 1 df, p=0.5922
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD IFNAR1 

TCGA-COAD IFNAR1 

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

n=4, 20 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     2.33    0.0476     0.154
strata=LOW  2        2     1.67    0.0667     0.154

 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 0.4812    1.6180   1.2380 0.389 0.697

Likelihood ratio test=0.16  on 1 df, p=0.6913
n= 4, number of events= 4 
   (20 observations deleted due to missingness)


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

TCGA-COAD XIAP 

TCGA-COAD XIAP 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     3.57     0.090     0.265
strata=LOW  3        3     2.43     0.132     0.265

 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 0.4730    1.6048   0.9269 0.51 0.61

Likelihood ratio test=0.27  on 1 df, p=0.6059
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD VDAC3 

TCGA-COAD VDAC3 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     5.07     0.845      2.97
strata=LOW  5        5     2.93     1.463      2.97

 Chisq= 3  on 1 degrees of freedom, p= 0.08 

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

           coef exp(coef) se(coef)     z     p
strataLOW 1.713     5.547    1.108 1.546 0.122

Likelihood ratio test=3.22  on 1 df, p=0.07281
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD CAMK2A 

TCGA-COAD CAMK2A 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2    0.983     1.051      1.59
strata=LOW  4        4    5.017     0.206      1.59

 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 -1.4388    0.2372   1.2359 -1.164 0.244

Likelihood ratio test=1.46  on 1 df, p=0.2262
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD VDAC1 

TCGA-COAD VDAC1 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 1        1     2.28     0.721      2.01
strata=LOW  4        4     2.72     0.606      2.01

 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 2.077e+01 1.049e+09 2.244e+04 0.001 0.999

Likelihood ratio test=3.22  on 1 df, p=0.07279
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD RIPK3 

TCGA-COAD RIPK3 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     2.69     0.175     0.339
strata=LOW  5        5     4.31     0.109     0.339

 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 0.5152    1.6740   0.8935 0.577 0.564

Likelihood ratio test=0.35  on 1 df, p=0.5557
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD CAPN1 

TCGA-COAD CAPN1 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     4.02     0.257     0.899
strata=LOW  3        3     1.98     0.521     0.899

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

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

            coef exp(coef) se(coef)     z     p
strataLOW 0.8606    2.3646   0.9325 0.923 0.356

Likelihood ratio test=0.87  on 1 df, p=0.3496
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD USP21 

TCGA-COAD USP21 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     5.59     0.451      2.82
strata=LOW  3        3     1.41     1.786      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 1.744     5.719    1.166 1.496 0.135

Likelihood ratio test=2.68  on 1 df, p=0.1015
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD AIFM1 

TCGA-COAD AIFM1 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     3.45    0.0575     0.142
strata=LOW  4        4     3.55    0.0558     0.142

 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 0.3150    1.3703   0.8385 0.376 0.707

Likelihood ratio test=0.14  on 1 df, p=0.7076
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD TRADD 

TCGA-COAD TRADD 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     4.55    0.0665     0.871
strata=LOW  1        1     0.45    0.6722     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 1.242     3.464    1.418 0.876 0.381

Likelihood ratio test=0.72  on 1 df, p=0.395
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD OPTN 

TCGA-COAD OPTN 

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

n=4, 20 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     1.33     0.333     0.615
strata=LOW  2        2     2.67     0.167     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 -0.9406    0.3904   1.2403 -0.758 0.448

Likelihood ratio test=0.62  on 1 df, p=0.4325
n= 4, number of events= 4 
   (20 observations deleted due to missingness)


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

TCGA-COAD PPID 

TCGA-COAD PPID 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     4.55    0.0665     0.871
strata=LOW  1        1     0.45    0.6722     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 1.242     3.464    1.418 0.876 0.381

Likelihood ratio test=0.72  on 1 df, p=0.395
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD RIPK1 

TCGA-COAD RIPK1 



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

TCGA-COAD TLR3 

TCGA-COAD TLR3 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     0.65     2.804      4.26
strata=LOW  3        3     4.35     0.419      4.26

 Chisq= 4.3  on 1 degrees of freedom, p= 0.04 

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

                coef  exp(coef)   se(coef)      z     p
strataLOW -2.232e+01  2.031e-10  3.308e+04 -0.001 0.999

Likelihood ratio test=4.61  on 1 df, p=0.03188
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD FAF1 

TCGA-COAD FAF1 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     4.45    0.0464     0.135
strata=LOW  4        4     3.55    0.0583     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 0.2873    1.3329   0.7856 0.366 0.715

Likelihood ratio test=0.13  on 1 df, p=0.7165
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD JAK1 

TCGA-COAD JAK1 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     2.74     0.198     0.466
strata=LOW  5        5     4.26     0.127     0.466

 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 0.7485    2.1137   1.1196 0.668 0.504

Likelihood ratio test=0.52  on 1 df, p=0.4724
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

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

significant_genes

[1] "RBCK1" "TRAF5" "TRAF2" "PGAM5" "TLR3" 

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 RBCK1 

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

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     4.35     0.419      4.26
strata=LOW  2        2     0.65     2.804      4.26

 Chisq= 4.3  on 1 degrees of freedom, p= 0.04 

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

               coef exp(coef)  se(coef)     z     p
strataLOW 2.232e+01 4.923e+09 3.308e+04 0.001 0.999

Likelihood ratio test=4.61  on 1 df, p=0.03188
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

TCGA-COAD TRAF5 

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

n=8, 16 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 3        3     1.03     3.788      5.28
strata=LOW  5        5     6.97     0.558      5.28

 Chisq= 5.3  on 1 degrees of freedom, p= 0.02 

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

             coef exp(coef) se(coef)      z      p
strataLOW -2.2495    0.1055   1.1729 -1.918 0.0551

Likelihood ratio test=4.43  on 1 df, p=0.03535
n= 8, number of events= 8 
   (16 observations deleted due to missingness)


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

TCGA-COAD TRAF2 

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

n=6, 18 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4    5.467     0.393      5.63
strata=LOW  2        2    0.533     4.033      5.63

 Chisq= 5.6  on 1 degrees of freedom, p= 0.02 

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

               coef exp(coef)  se(coef)     z     p
strataLOW 2.195e+01 3.399e+09 2.380e+04 0.001 0.999

Likelihood ratio test=5.42  on 1 df, p=0.01995
n= 6, number of events= 6 
   (18 observations deleted due to missingness)


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

TCGA-COAD PGAM5 

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

n=7, 17 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 4        4     5.79     0.552      3.92
strata=LOW  3        3     1.21     2.638      3.92

 Chisq= 3.9  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 2.008     7.449    1.172 1.713 0.0867

Likelihood ratio test=3.53  on 1 df, p=0.06043
n= 7, number of events= 7 
   (17 observations deleted due to missingness)


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

TCGA-COAD TLR3 

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

n=5, 19 observations deleted due to missingness.

            N Observed Expected (O-E)^2/E (O-E)^2/V
strata=HIGH 2        2     0.65     2.804      4.26
strata=LOW  3        3     4.35     0.419      4.26

 Chisq= 4.3  on 1 degrees of freedom, p= 0.04 

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

                coef  exp(coef)   se(coef)      z     p
strataLOW -2.232e+01  2.031e-10  3.308e+04 -0.001 0.999

Likelihood ratio test=4.61  on 1 df, p=0.03188
n= 5, number of events= 5 
   (19 observations deleted due to missingness)


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

---

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

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