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Comprehensive ceRNA Profiling Uncovers Clinically Relevant Hub lncRNAs in Glioblastoma.

Code ↔ Paper

16 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 16 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Data acquisition and processing ↔ 01_data_download.R, the whole file · a weak match · score 0.92 · Solid Tissue Normal, TCGAbiolinks, GENCODE v38, GTEx brain, gene expression, query
  2. [2] § Methods › Functional enrichment analysis ↔ 11_prognostic_lncRNA_analysis_correlation_biopathways.R, lines 46–87 · score 0.82 · GO BP, Entrez IDs, KEGG pathways, Biological Process, enrichment, mRNA
  3. [3] § Methods › Differential expression analysis ↔ 03_diff_expression.R, lines 70–138 · score 0.79 · eBayes, design matrix, limma, DEGs, fold, fitted
  4. [4] § Methods › Integration of experimentally validated interactions ↔ 06_process_mirtarbase_ceRNA.R, the whole file · a weak match · score 0.77 · miRBaseConverter, Ensembl gene IDs, miRTarBase, Mature, precursor, human
  5. [5] § Methods › Data normalization and filtering ↔ 03_diff_expression.R, lines 70–138 · score 0.76 · model.matrix, batch corrected, design matrix, limma, quality, Voom
  6. [6] § Methods › Data acquisition and processing ↔ 05_extract_gene_info.R, the whole file · a weak match · score 0.72 · GENCODE v38, gene annotation, external gene, Ensembl gene, rtracklayer, downloaded
  7. [7] § Methods › Integration of experimentally validated interactions ↔ 07_process_encori_ceRNA.R, the whole file · a weak match · score 0.70 · miRBaseConverter, CLIP supported, Mature, Ensembl gene, precursor, mapped
  8. [8] § Results › Pathway enrichment analysis using GO and KEGG databases ↔ 11_prognostic_lncRNA_analysis_correlation_biopathways.R, lines 46–87 · score 0.65 · KEGG pathway enrichment, GO biological process, matrix, mRNAs, genes
  9. [9] § Results › Data acquisition, normalization and quality control for downstream analysis ↔ 13_harmonizationsummary_QC.R, lines 65–119 · score 0.63 · log2 CPM, miRNAs, GTEx normal, TCGA normal, TCGA GBM, median
  10. [10] § Results › Expression and survival analysis of ceRNA hub lncRNAs ↔ 10_unique_lncRNAs_survivalplots.R, the whole file · a weak match · score 0.57 · Kaplan Meier survival, primary tumor, clinical, tumor samples, ceRNA, lncRNA
  11. [11] § Results › Expression and survival analysis of ceRNA hub lncRNAs ↔ 12_survivalplots_ceRNA_mRNAs.R, lines 1–40 · score 0.55 · Kaplan Meier survival, primary tumor, clinical, tumor samples, TCGA GBM, ceRNA
  12. [12] § Results › Differential expression analysis of genes between different databases ↔ 13_harmonizationsummary_QC.R, lines 65–119 · score 0.54 · log2 CPM, miRNAs, GTEx normal, TCGA normal, tumor samples, TCGA GBM
  13. [13] § Results › ceRNA triplet (lncRNA -| miRNA -> mRNA) construction ↔ 08_ceRNA_triplet_construction.R, lines 89–160 · score 0.53 · validated mRNAs, miRNAs, downregulated, triplets, upregulated, intersection
  14. [14] § Methods › Construction of the GBM-specific ceRNA network ↔ 08_ceRNA_triplet_construction.R, lines 89–160 · score 0.53 · miRNAs, mRNAs, downregulated, upregulated, intersected, triplets
  15. [15] § Methods › Survival analysis ↔ 10_unique_lncRNAs_survivalplots.R, the whole file · a weak match · score 0.53 · Kaplan Meier, median expression, survival, lncRNA, TCGA
  16. [16] § Results › Co-expression analysis between CYTOR and MIR4435-2HG in TCGA-GBM cohort ↔ 11_prognostic_lncRNA_analysis_correlation_biopathways.R, lines 184–227 · score 0.51 · HG expression, GBM tumor samples, R2, correlation, Pearson, CYTOR

Paper

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The authors' code

R · 228 lines · 7.2 KB · no license · 3 matches

  1. head(ceRNA_triplets)
  3. # Using Ensembl ID for CYTOR/MIR4435-2HG
  4. ceRNA_CYTOR <- ceRNA_triplets %>% filter(lncRNA == "ENSG00000222041")
  5. ceRNA_MIR44352HG <- ceRNA_triplets %>% filter(lncRNA == "ENSG00000172965")
  7. head(ceRNA_MIR44352HG)
  8. head(ceRNA_CYTOR)
  10. # Get unique mRNAs (Ensembl IDs)
  11. mRNAs_CYTOR <- unique(ceRNA_CYTOR$mRNA)
  12. mRNAs_mir44352HG <- unique(ceRNA_MIR44352HG$mRNA)
  14. lnc_ids <- c("ENSG00000222041", "ENSG00000172965")
  16. library(dplyr)
  17. # This makes sure you're using dplyr's select:
  18. all_partners <- ceRNA_triplets %>%
  19. filter(lncRNA %in% c("ENSG00000222041", "ENSG00000172965")) %>%
  20. dplyr::select(lncRNA, miRNA, mRNA) %>%
  21. distinct() %>%
  22. arrange(lncRNA, miRNA, mRNA)
  24. expr_cytor <- as.numeric(expr_mat["ENSG00000222041", ])
  25. expr_mir4435hg <- as.numeric(expr_mat["ENSG00000172965", ])
  27. # Pearson correlation (linear)
  28. cor_test <- cor.test(expr_cytor, expr_mir4435hg, method = "pearson")
  29. print(cor_test)
  31. # For non-normal data, Spearman can be used:
  32. # cor.test(expr_cytor, expr_mir4435hg, method = "spearman")
  34. library(ggplot2)
  35. df <- data.frame(CYTOR = expr_cytor, MIR4435_1HG = expr_mir4435hg)
  36. ggplot(df, aes(x = CYTOR, y = MIR4435_1HG)) +
  37. geom_point() +
  38. geom_smooth(method = "lm", se = FALSE, col = "red") +
  39. labs(
  40. x = "CYTOR (ENSG00000222041) Expression",
  41. y = "MIR4435-1HG (ENSG00000172965) Expression",
  42. title = "Correlation of lncRNA Expression (GBM Tumor Samples)"
  43. ) +
  44. theme_minimal(base_size = 14)
  46. # Assuming ceRNA_triplets contains your filtered results
  47. shared_mRNAs <- ceRNA_triplets %>%
  48. filter(lncRNA %in% c("ENSG00000222041", "ENSG00000172965")) %>%
  49. pull(mRNA) %>%
  50. unique()
  51. length(shared_mRNAs)
  52. head(shared_mRNAs)
  54. library(clusterProfiler)
  55. library(org.Hs.eg.db)
  57. # Convert Ensembl IDs to Entrez IDs
  58. entrez_ids <- mapIds(org.Hs.eg.db, keys=shared_mRNAs, column="ENTREZID", keytype="ENSEMBL", multiVals="first")
  59. entrez_ids <- na.omit(entrez_ids)
  61. # KEGG pathway enrichment
  62. kegg_res <- enrichKEGG(gene=entrez_ids, organism="hsa", pvalueCutoff=0.05)
  63. head(kegg_res)
  65. mrna_annot <- gene_info %>%
  66. filter(ensembl_gene_id %in% shared_mRNAs, gene_biotype == "protein_coding")
  67. gene_symbols <- mrna_annot$external_gene_name
  68. print(gene_symbols)
  70. # GO Biological Process
  71. go_bp <- enrichGO(gene=entrez_ids, OrgDb=org.Hs.eg.db, ont="BP", pAdjustMethod="BH", pvalueCutoff=0.05, readable=TRUE)
  72. head(go_bp)
  73. head(gene_info)
  74. library(clusterProfiler)
  75. gene_symbols <- mRNA_annot$external_gene_name
  76. gene_desc <- bitr(gene_symbols, fromType="SYMBOL", toType=c("GENENAME"), OrgDb="org.Hs.eg.db")
  77. head(gene_desc)
  79. # Set Ensembl IDs
  80. cytor_id <- "ENSG00000222041"
  81. mir4435hg_id <- "ENSG00000172965"
  83. # --------- Correlation Plot: CYTOR vs MIR4435-2HG ---------
  84. # Required: expr_mat (normalized matrix, Ensembl IDs as rownames)
  86. library(ggplot2)
  87. library(tibble)
  89. # 1. Set Ensembl IDs
  90. cytor_id <- "ENSG00000222041"
  91. mir4435hg_id <- "ENSG00000172965"
  93. # 2. Extract expression values (all samples or tumor subset as needed)
  94. expr_cytor <- expr_mat[cytor_id, ]
  95. expr_mir4435hg <- expr_mat[mir4435hg_id, ]
  97. # 3. Build a data frame
  98. df_corr <- tibble(
  99. CYTOR = as.numeric(expr_cytor),
  100. MIR4435_2HG = as.numeric(expr_mir4435hg)
  101. )
  103. # 4. Pearson correlation test
  104. cor_test <- cor.test(df_corr$CYTOR, df_corr$MIR4435_2HG, method = "pearson")
  105. cor_val <- round(cor_test$estimate, 3)
  106. p_lab <- ifelse(cor_test$p.value < 2e-16, "p < 2e-16", paste0("p = ", signif(cor_test$p.value, 3)))
  108. # 5. Plot with ggplot2
  109. p <- ggplot(df_corr, aes(x = CYTOR, y = MIR4435_2HG)) +
  110. geom_point(color = "#3182bd", alpha = 0.7, size = 2) +
  111. geom_smooth(method = "lm", color = "#e6550d", fill = "#e6550d", alpha = 0.2) +
  112. theme_bw(base_size = 15) +
  113. labs(
  114. x = "CYTOR expression (logCPM, normalized)",
  115. y = "MIR4435-2HG expression (logCPM, normalized)",
  116. title = "Correlation between CYTOR and MIR4435-2HG expression"
  117. ) +
  118. annotate(
  119. "text",
  120. x = min(df_corr$CYTOR, na.rm = TRUE),
  121. y = max(df_corr$MIR4435_2HG, na.rm = TRUE),
  122. hjust = 0, vjust = 1,
  123. label = paste0("Pearson's r = ", cor_val, "\n", p_lab),
  124. size = 5,
  125. color = "black"
  126. )
  128. # 6. Save and show plot
  129. ggsave("correlation_CYTOR_MIR44352HG.pdf", p, width = 6, height = 5)
  130. print(p)
  133. length(unique(ceRNA_triplets$mRNA))
  135. # --- Correlation: CYTOR vs MIR4435-2HG in GBM Tumors ---
  137. # Ensembl IDs
  138. cytor_id <- "ENSG00000222041"
  139. mir4435hg_id <- "ENSG00000172965"
  141. # Check both genes exist
  142. if (!(cytor_id %in% rownames(expr_mat)) | !(mir4435hg_id %in% rownames(expr_mat))) {
  143. stop("One or both lncRNAs not found in expression matrix.")
  144. }
  146. # Expression in tumor samples (already subset to tumor columns via expr_mat_tumor)
  147. expr_cytor <- expr_mat[cytor_id, tumor_samples]
  148. expr_mir4435hg <- expr_mat[mir4435hg_id, tumor_samples]
  150. # Correlation test (Pearson)
  151. cor_test <- cor.test(expr_cytor, expr_mir4435hg, method = "pearson")
  152. cat("Pearson correlation between CYTOR and MIR4435-2HG:\n")
  153. print(cor_test)
  155. # Create plot
  156. library(ggplot2)
  157. library(tibble)
  159. df_corr <- tibble(
  160. CYTOR = as.numeric(expr_cytor),
  161. MIR4435_2HG = as.numeric(expr_mir4435hg)
  162. )
  164. p_corr <- ggplot(df_corr, aes(x = CYTOR, y = MIR4435_2HG)) +
  165. geom_point(color = "#2c7fb8", alpha = 0.7, size = 2) +
  166. geom_smooth(method = "lm", color = "#d95f0e", se = FALSE) +
  167. theme_bw(base_size = 14) +
  168. labs(
  169. title = "CYTOR vs MIR4435-2HG in GBM Tumor Samples",
  170. x = "CYTOR expression (logCPM)",
  171. y = "MIR4435-2HG expression (logCPM)"
  172. ) +
  173. annotate("text",
  174. x = min(df_corr$CYTOR, na.rm = TRUE),
  175. y = max(df_corr$MIR4435_2HG, na.rm = TRUE),
  176. label = paste0("r = ", round(cor_test$estimate, 3), "\n",
  177. "p = ", signif(cor_test$p.value, 3)),
  178. hjust = 0, vjust = 1,
  179. size = 5)
  181. ggsave("correlation_CYTOR_vs_MIR4435-2HG.pdf", plot = p_corr, width = 6, height = 5)
  184. # --- Correlation: CYTOR vs MIR4435-2HG in GBM Tumors (with R²) ---
  186. cytor_id <- "ENSG00000222041"
  187. mir4435hg_id <- "ENSG00000172965"
  189. if (!(cytor_id %in% rownames(expr_mat)) | !(mir4435hg_id %in% rownames(expr_mat))) {
  190. stop("One or both lncRNAs not found in expression matrix.")
  191. }
  193. expr_cytor <- expr_mat[cytor_id, tumor_samples]
  194. expr_mir4435hg <- expr_mat[mir4435hg_id, tumor_samples]
  196. cor_test <- cor.test(expr_cytor, expr_mir4435hg, method = "pearson")
  197. r_val <- round(as.numeric(cor_test$estimate), 3)
  198. r2_val <- round(r_val^2, 3) # Calculate R²
  199. p_lab <- ifelse(cor_test$p.value < 2e-16, "p < 2e-16", paste0("p = ", signif(cor_test$p.value, 3)))
  201. df_corr <- tibble(
  202. CYTOR = as.numeric(expr_cytor),
  203. MIR4435_2HG = as.numeric(expr_mir4435hg)
  204. )
  206. # Create annotation label
  207. corr_label <- paste0("r = ", r_val,
  208. "\nR² = ", r2_val,
  209. "\n", p_lab)
  211. p_corr <- ggplot(df_corr, aes(x = CYTOR, y = MIR4435_2HG)) +
  212. geom_point(color = "#2c7fb8", alpha = 0.7, size = 2) +
  213. geom_smooth(method = "lm", color = "#d95f0e", se = FALSE) +
  214. theme_bw(base_size = 14) +
  215. labs(
  216. title = "CYTOR vs MIR4435-2HG in GBM Tumor Samples",
  217. x = "CYTOR expression (logCPM)",
  218. y = "MIR4435-2HG expression (logCPM)"
  219. ) +
  220. annotate("text",
  221. x = min(df_corr$CYTOR, na.rm = TRUE),
  222. y = max(df_corr$MIR4435_2HG, na.rm = TRUE),
  223. label = corr_label,
  224. hjust = 0, vjust = 1,
  225. size = 5)
  227. ggsave("correlation_CYTOR_vs_MIR4435-2HG.pdf", plot = p_corr, width = 6, height = 5)

11_prognostic_lncRNA_analysis_correlation_biopathways.R at commit a2348ad, no license · at the source

Overview

Authors: Zohaib Rana1, Joke Grans1
ORCID iDs: Zohaib Rana
  1. Department of Biochemistry, University of Otago, Dunedin, New Zealand
Institutions: University of Otago (New Zealand)
Journal: Bioinformatics and biology insights, volume 20, article 11779322251411169
Dates: received 4 August 2025; accepted 11 December 2025; published online 9 April 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1177/11779322251411169 · PMID 41978797 · PMCID PMC13070187 · OpenAlex W7152637293
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), other condition (population), clinical / translational (subfield)
Methods: Statistics
Keywords: Glioblastoma (GBM), long non-coding RNA (lncRNA), competing endogenous RNA (ceRNA) network, RNA-seq integrative analysis, prognostic biomarkers
Topic: Cancer-related molecular mechanisms research (Cancer Research, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 58 references in the paper

Abstract

Long non-coding RNAs (lncRNAs) can function as competing endogenous RNAs (ceRNAs) that rewire post-transcriptional regulation in glioblastoma (GBM). Previous GBM studies have focused on either single lncRNA ceRNA axis in isolation or used in silico predictions with small patient cohorts (< 200). In this study, we integrated RNA-seq data from 372 TCGA-GBM tumors, 5 matched adjacent TCGA-normal brain and 2 931 GTEx-normal brain (n = 3 308) samples to build an experimentally informed ceRNA atlas. Limma-voom differential analysis, intersection with 2 experimentally supported interaction databases (ENCORI and miRTarBase) distilled 517 high-confidence lncRNA–miRNA–mRNA triplets. Twelve hub lncRNAs coordinated 3 downregulated miRNAs and 262 target mRNAs enriched for cell-cycle, p53 signaling and homologous recombination pathways. Two co-expressed hubs, CYTOR and MIR4435-2HG, were significantly over-expressed in GBM tumors in comparison with normal brain tissue and independently predicted poor overall survival (log-rank P < .01). Their shared 25 targets include oncogenic YBX1, MDM4 and TGFBR1 mRNAs, underscoring the redundant regulation of oncogenic pathways, suggesting the need to explore combination lncRNA inhibition strategies. This population-scale analysis prioritizes CYTOR and MIR4435-2HG for functional interrogation and offers a framework for exploring biomarkers and RNA-targeted strategies in GBM.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 16 matches between paragraphs and lines of code.

zz153/braincancer-ceRNA-network

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: a2348adc9183da2939c26260a040e35245377122, 28 April 2026
Languages: R (12), Shell (1)
Size: 14 files, 13 scripts
Software Heritage: not archived
Found in: the text, “Footnotes”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (10 files), ggplot2 (3 files), survival (3 files), clusterProfiler (2 files), ggpubr (2 files), edgeR (1 file), limma (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
14 files
At the source: github.com/zz153/braincancer-ceRNA-network

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 13 scripts, each with its path and the digest of its content;
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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 5 keywords, 58 references.

Cite

This paper

Rana, Z., & Grans, J. (2026). Comprehensive ceRNA Profiling Uncovers Clinically Relevant Hub lncRNAs in Glioblastoma. Bioinformatics and biology insights, 20, 11779322251411169. https://doi.org/10.1177/11779322251411169

BibTeX

@article{rana2026comprehensive,
author = {Rana, Zohaib and Grans, Joke},
title = {{Comprehensive ceRNA Profiling Uncovers Clinically Relevant Hub lncRNAs in Glioblastoma}},
journal = {Bioinformatics and biology insights},
year = {2026},
month = apr,
volume = {20},
pages = {11779322251411169},
publisher = {SAGE Publications},
issn = {1177-9322},
doi = {10.1177/11779322251411169},
url = {https://doi.org/10.1177/11779322251411169},
pmid = {41978797},
pmcid = {PMC13070187}
}

RIS

TY - JOUR
AU - Rana, Zohaib
AU - Grans, Joke
TI - Comprehensive ceRNA Profiling Uncovers Clinically Relevant Hub lncRNAs in Glioblastoma
T2 - Bioinformatics and biology insights
J2 - Bioinform Biol Insights
PY - 2026
DA - 2026/04/09
VL - 20
SP - 11779322251411169
SN - 1177-9322
PB - SAGE Publications
DO - 10.1177/11779322251411169
UR - https://doi.org/10.1177/11779322251411169
LA - en
ER -

CSL-JSON

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"title": "Comprehensive ceRNA Profiling Uncovers Clinically Relevant Hub lncRNAs in Glioblastoma",
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"container-title-short": "Bioinform Biol Insights",
"volume": "20",
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"DOI": "10.1177/11779322251411169",
"PMID": "41978797",
"PMCID": "PMC13070187",
"ISSN": "1177-9322",
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"language": "en",
"issued": {
"date-parts": [
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2026,
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]
}
}

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