OSCR

Loss of Neuropeptide Y Signaling Accompanies the Neural-to-Mesenchymal Transcriptional Transition in Glioblastoma: A Multi-Scale Transcriptomic Analysis.

Code ↔ Paper

20 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 20 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § 4. Materials and Methods › 4.4. Survival Analysis ↔ R/02_survival_lasso_cox.R, lines 46–120 · score 1.00 · zero LASSO coefficients, LASSO penalized Cox, composite risk score, Multivariate Cox, Univariate Cox, Kaplan Meier
  2. [2] § 4. Materials and Methods › 4.11. Composition Correction and Spatial Cross-Correlation ↔ R/09_composition_moran.R, lines 1–24 · score 0.99 · gene intrinsic regulation, analytical composition correction, composition expected log2FC, composition shift, cross correlation, f_GBM
  3. [3] § 4. Materials and Methods › 4.10. Transcription Factor Activity and Co-Expression Network Analysis ↔ R/08_tf_network.R, lines 1–73 · score 0.99 · VIPER TF activity, DoRothEA, co expression network, co expressed genes, integrative multi omic, Louvain community
  4. [4] § 4. Materials and Methods › 4.8. Tumor Microenvironment and Cell–Cell Communication ↔ R/00_setup.R, lines 49–97 · score 0.98 · HLA DRA, IL1B, AddModuleScore, Macrophage polarization, ligand receptor, NPY2R
  5. [5] § 4. Materials and Methods › 4.3. TCGA Bulk RNA-Seq Differential Expression Analysis ↔ R/01_tcga_dea.R, lines 1–22 · score 0.95 · TCGAbiolinks, edgeR, Classical GBM, Proneural GBM, RNA seq, TCGA LGG
  6. [6] § 4. Materials and Methods › 4.1. Datasets and Specimen Provenance ↔ R/01_tcga_dea.R, lines 1–22 · score 0.94 · GTEx v8 normal, frontal cortex, PanCancer, cBioPortal, TCGA LGG, TCGA GBM
  7. [7] § 4. Materials and Methods › 4.10. Transcription Factor Activity and Co-Expression Network Analysis ↔ R/00_setup.R, lines 49–97 · score 0.93 · NFE2L2, axis relevant, HIF1A, Transcription factor, ARNT, ETS1
  8. [8] § 4. Materials and Methods › 4.7. Single-Cell RNA-Seq Analysis ↔ R/05_scrnaseq.R, lines 1–30 · score 0.91 · adult IDH, DoubletFinder, SCTransform, UMAP, module score, Seurat
  9. [9] § 4. Materials and Methods › 4.9. Spatial Transcriptomics ↔ R/07_spatial.R, lines 1–29 · score 0.91 · Kruskal Wallis, SPOTlight, GBM patients, SCTransform, Hypoxia module scores, UMAP
  10. [10] § 4. Materials and Methods › 4.8. Tumor Microenvironment and Cell–Cell Communication ↔ R/06_tme_cellchat.R, lines 1–41 · score 0.90 · macrophage annotated cells, M2 signature, AddModuleScore, cell communication, CellChat, ligand receptor
  11. [11] § 4. Materials and Methods › 4.5. Pathway Enrichment Analysis ↔ R/03_pathway_enrichment.R, lines 1–30 · score 0.88 · GO Biological Process, clusterProfiler, log2FC, msigdbr, simplified, ORA
  12. [12] § 2. Results › 2.6. Tumor Microenvironment: Macrophage Polarization and Intercellular Communication ↔ R/06_tme_cellchat.R, lines 43–94 · score 0.81 · VEGFA FLT1, communication probabilities, NPY5R, ligand receptor, NPY2R, S18
  13. [13] § 2. Results › 2.6. Tumor Microenvironment: Macrophage Polarization and Intercellular Communication ↔ R/06_tme_cellchat.R, lines 1–41 · score 0.80 · M2 scoring, CellChat, ligand receptor, S14, S15, S16
  14. [14] § 2. Results › 2.8. Transcription Factor Activity and Co-Expression Network Analysis Identifies Regulatory Hubs ↔ R/08_tf_network.R, lines 1–73 · score 0.79 · VIPER TF activity, integrative multi omic, network communities, log2FC, heatmap, clustered
  15. [15] § 4. Materials and Methods › 4.6. External Validation and Meta-Analysis ↔ R/04_meta_analysis.R, lines 20–37 · score 0.73 · quantile normalized, log2 transformed, best probe, collapse, mapping, meta
  16. [16] § 2. Results › 2.2. Prognostic Significance of NPY-Panel Genes in GBM ↔ R/02_survival_lasso_cox.R, lines 46–120 · score 0.66 · LASSO penalized Cox, dependent ROC, AUC, risk, survival, gene
  17. [17] § 2. Results › 2.7. Spatial Transcriptomics Maps NPY and Hypoxia to Weakly Inversely Correlated Tumor Zones ↔ R/09_composition_moran.R, lines 1–24 · score 0.66 · cross correlation, bivariate Moran, Hypoxia Core, Visium, composition, Glycolysis
  18. [18] § 2. Results › 2.7. Spatial Transcriptomics Maps NPY and Hypoxia to Weakly Inversely Correlated Tumor Zones ↔ R/07_spatial.R, lines 1–29 · score 0.63 · SPOTlight, GBM patients, module score, GSE194329, S7, S6
  19. [19] § 2. Results › 2.3. Pathway Enrichment Identifies Hypoxia, EMT, and Immune Signaling as Central Programs ↔ R/03_pathway_enrichment.R, lines 1–30 · score 0.61 · GO Biological Process, DEGs, enrichment, KEGG, S9, S7
  20. [20] § 4. Materials and Methods › 4.12. Software and Reproducibility ↔ install_packages.R, the whole file · a weak match · score 0.61 · SPOTlight, CellChat, fgsea, glmnet, v1, metafor

Paper

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The authors' code

R · 137 lines · 6 KB · MIT · 3 matches

  1. # =============================================================================
  2. # 06_tme_cellchat.R
  3. # Tumor microenvironment + cell-cell communication (Methods 4.8)
  4. #
  5. # Produces:
  6. # Table S13 - Macrophage M1/M2 polarization module scores (per cell)
  7. # Table S14 - CellChat significant L-R interactions (all pathways)
  8. # Table S15 - CellChat significant interactions involving NPY-panel genes
  9. # Table S16 - CellChat interaction COUNT matrix (cell-type pairs)
  10. # Table S17 - CellChat interaction WEIGHT/strength matrix (cell-type pairs)
  11. # Table S18 - Custom NPY ligand-receptor permutation test
  12. # Figure S5 - TME and intercellular communication
  13. # =============================================================================
  15. source("R/00_setup.R")
  16. need(c("Seurat", "CellChat", "dplyr"))
  17. library(Seurat); library(CellChat); library(dplyr)
  19. seu <- readRDS(file.path(DIR_PROC, "scrna_seurat.rds"))
  21. # -----------------------------------------------------------------------------
  22. # 1. Macrophage M1/M2 polarization (Table S13)
  23. # AddModuleScore on macrophage-annotated cells using the M1/M2 signatures.
  24. # -----------------------------------------------------------------------------
  25. mac <- subset(seu, subset = cell_type == "Macrophage")
  26. mac <- AddModuleScore(mac, features = list(M1 = M1_SIG), name = "M1_", seed = SEED)
  27. mac <- AddModuleScore(mac, features = list(M2 = M2_SIG), name = "M2_", seed = SEED)
  28. # Also carry the seven NPY module scores per macrophage (npy_mod_1..7)
  29. mac <- AddModuleScore(mac, features = MODULE_SIGNATURES, name = "npy_mod_", seed = SEED)
  31. s13 <- data.frame(
  32. ID = colnames(mac),
  33. sample = mac$sample,
  34. seurat_clusters = mac$seurat_clusters,
  35. M1_score = mac$M1_1,
  36. M2_score = mac$M2_1
  37. )
  38. npy_mod <- [email hidden][, paste0("npy_mod_", seq_along(MODULE_SIGNATURES))]
  39. colnames(npy_mod) <- paste0("npy_mod_", seq_along(MODULE_SIGNATURES))
  40. s13 <- cbind(s13, npy_mod)
  41. save_table(s13, "TableS13_macrophage_M1_M2_polarization.csv")
  43. # -----------------------------------------------------------------------------
  44. # 2. CellChat v1.5 with human CellChatDB (Methods 4.8)
  45. # -----------------------------------------------------------------------------
  46. data.input <- GetAssayData(seu, assay = "SCT", slot = "data")
  47. meta.cc <- data.frame(labels = seu$cell_type, row.names = colnames(seu))
  48. cc <- createCellChat(object = data.input, meta = meta.cc, group.by = "labels")
  49. cc@DB <- CellChatDB.human
  51. cc <- subsetData(cc)
  52. cc <- identifyOverExpressedGenes(cc)
  53. cc <- identifyOverExpressedInteractions(cc)
  54. cc <- computeCommunProb(cc) # seed fixed globally (42)
  55. cc <- filterCommunication(cc, min.cells = 10)
  56. cc <- computeCommunProbPathway(cc)
  57. cc <- aggregateNet(cc)
  59. # ---- Table S14: all significant L-R interactions (p < 0.05) ----------------
  60. lr_all <- subsetCommunication(cc) # data.frame of significant pairs
  61. lr_all <- lr_all[lr_all$pval < 0.05, ]
  62. save_table(lr_all, "TableS14_CellChat_all_LR_interactions.csv")
  64. # ---- Table S15: interactions involving NPY-panel genes ---------------------
  65. npy_rel <- lr_all[ lr_all$ligand %in% NPY_PANEL |
  66. lr_all$receptor %in% NPY_PANEL, ]
  67. save_table(npy_rel, "TableS15_CellChat_NPYpanel_interactions.csv")
  69. # ---- Tables S16 / S17: count and weight matrices over cell-type pairs ------
  70. count_mat <- as.data.frame(cc@net$count)
  71. weight_mat <- as.data.frame(cc@net$weight)
  72. count_mat <- cbind(sender = rownames(count_mat), count_mat)
  73. weight_mat <- cbind(sender = rownames(weight_mat), weight_mat)
  74. save_table(count_mat, "TableS16_CellChat_interaction_count_matrix.csv")
  75. save_table(weight_mat, "TableS17_CellChat_interaction_weight_matrix.csv")
  77. # -----------------------------------------------------------------------------
  78. # 3. Custom NPY ligand-receptor permutation test (Table S18)
  79. # CellChatDB lacks NPY-NPY1R/NPY2R pairs, so we test them directly:
  80. # communication probability = mean ligand expr (sender) x mean receptor
  81. # expr (receiver), with significance from label permutation (Methods 4.8 /
  82. # Limitations). Includes positive-control pairs (e.g. VEGFA-FLT1).
  83. # -----------------------------------------------------------------------------
  84. expr_mat <- as.matrix(GetAssayData(seu, assay = "SCT", slot = "data"))
  85. cell_type <- seu$cell_type
  86. types <- sort(unique(cell_type))
  88. lr_pairs <- rbind(
  89. data.frame(Ligand = "NPY", Receptor = "NPY1R", Pair_Type = "NPY"),
  90. data.frame(Ligand = "NPY", Receptor = "NPY2R", Pair_Type = "NPY"),
  91. data.frame(Ligand = "NPY", Receptor = "NPY5R", Pair_Type = "NPY"),
  92. data.frame(Ligand = "VEGFA", Receptor = "FLT1", Pair_Type = "Control"),
  93. data.frame(Ligand = "SPP1", Receptor = "CD44", Pair_Type = "Control")
  94. )
  96. mean_expr <- function(gene, cells) {
  97. if (!gene %in% rownames(expr_mat)) return(NA_real_)
  98. mean(expr_mat[gene, cells])
  99. }
  100. pct_expr <- function(gene, cells) {
  101. if (!gene %in% rownames(expr_mat)) return(NA_real_)
  102. 100 * mean(expr_mat[gene, cells] > 0)
  103. }
  105. set.seed(SEED)
  106. NPERM <- 1000
  107. res <- list()
  108. for (i in seq_len(nrow(lr_pairs))) {
  109. L <- lr_pairs$Ligand[i]; R <- lr_pairs$Receptor[i]
  110. for (s in types) for (r in types) {
  111. cs <- names(cell_type)[cell_type == s]
  112. cr <- names(cell_type)[cell_type == r]
  113. lavg <- mean_expr(L, cs); ravg <- mean_expr(R, cr)
  114. if (is.na(lavg) || is.na(ravg)) next
  115. obs <- lavg * ravg
  116. # permutation null: shuffle cell-type labels
  117. null <- replicate(NPERM, {
  118. perm <- sample(cell_type)
  119. mean(expr_mat[L, names(perm)[perm == s]]) *
  120. mean(expr_mat[R, names(perm)[perm == r]])
  121. })
  122. res[[length(res) + 1]] <- data.frame(
  123. Ligand = L, Receptor = R, Sender = s, Receiver = r,
  124. Ligand_avgExpr = lavg, Ligand_pctExpr = pct_expr(L, cs),
  125. Receptor_avgExpr = ravg, Receptor_pctExpr = pct_expr(R, cr),
  126. Communication_Probability = obs,
  127. Pval = (sum(null >= obs) + 1) / (NPERM + 1),
  128. Pair_Type = lr_pairs$Pair_Type[i]
  129. )
  130. }
  131. }
  132. s18 <- bind_rows(res)
  133. s18$Padj <- p.adjust(s18$Pval, method = "BH")
  134. save_table(s18, "TableS18_NPY_LR_permutation_test.csv")
  136. saveRDS(cc, file.path(DIR_PROC, "cellchat.rds"))
  137. message("06_tme_cellchat.R complete: Tables S13-S18 + Figure S5 inputs written.")

06_tme_cellchat.R at commit caeb79c, under MIT · at the source

Overview

Authors: Fareeha Arshad1, Nouran Abualsaud2, Arshiya Akbar1, Mohammed Imran Khan3, Bushra Rasheed1, Adnan Hussain3, Fahad Ali Alghamdi3, Faisal Abdulhameed Farrash4, Edwin N. Aroke5, Khalid Walid Freij5, Itika Arora1, Ahmed Yaqinuddin1
ORCID iDs: Faisal Abdulhameed Farrash, Edwin N. Aroke, Khalid Walid Freij, Ahmed Yaqinuddin
  1. College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.); (A.A.); (B.R.)
  2. King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia
  3. King Faisal Specialist Hospital and Research Center, Jeddah 23433, Saudi Arabia; (M.I.K.); (A.H.); (F.A.A.)
  4. King Faisal Specialist Hospital and Research Center, Riyadh 11211, Saudi Arabia
  5. Department of Acute, Chronic & Continuing Care, School of Nursing, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (E.N.A.); (K.W.F.)
Institutions: Alfaisal University (Saudi Arabia); King Saud bin Abdulaziz University for Health Sciences (Saudi Arabia); King Abdullah International Medical Research Center (Saudi Arabia); King Faisal Specialist Hospital & Research Centre (Saudi Arabia); University of Alabama at Birmingham (United States)
Journal: International journal of molecular sciences, volume 27, issue 13, article 6068
Dates: received 20 April 2026; accepted 23 June 2026; published online 6 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/ijms27136068 · PMID 42450334 · PMCID PMC13361764 · OpenAlex W7167470281
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Connectivity
Keywords: angiogenesis, gene expression, glioblastoma, hypoxia, immune suppression, neuropeptide Y, single-cell RNA sequencing, spatial transcriptomics, transcriptomic analysis, tumor microenvironment
MeSH: Brain Neoplasms*, Glioblastoma*, Neuropeptide Y*, Signal Transduction*, Transcriptome*, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Humans, Receptors, Neuropeptide Y, Single-Cell Gene Expression Analysis, Spatial Transcriptomics (* major topic)
Topic: Neuropeptides and Animal Physiology (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Neuropeptide Y [NPY; encoded by the NPY gene] is a widely expressed 36-amino-acid neuropeptide that regulates neuronal function, vascular regulation, and immune regulation; its role in glioblastoma [GBM] remains incompletely characterized. We performed an integrative in silico multi-scale transcriptomic analysis combining bulk RNA-sequencing of IDH-wildtype GBM [n = 169] and lower-grade glioma [n = 510] surgical resections from TCGA, normal cortical tissue from GTEx [n = 207], and four independent GEO validation cohorts of surgical GBM and non-tumor brain specimens [GSE4290, GSE50161, GSE131928 scRNA-seq of ~20,426 cells from 28 patients, and GSE194329 10X Visium spatial transcriptomics from five patients], along with survival modeling, pathway enrichment, single-cell RNA sequencing, spatial transcriptomics, and cell–cell communication analysis. NPY and its principal receptor, NPY1R, were significantly downregulated in GBM, while genes associated with hypoxia, angiogenesis, invasion, and immune suppression were upregulated. Single-cell analysis showed that NPY-axis transcript expression was elevated in neural progenitor-like populations. In contrast, hypoxia and metabolic programs were concentrated in mesenchymal tumors and stromal compartments, indicating distinct cellular contexts. Spatial analysis revealed a weak and heterogeneous relationship between NPY and hypoxia signatures, with substantial inter-patient variability and no significant global spatial cross-correlation. These findings indicate that loss of NPY signaling is a consistent feature of GBM and is associated with hypoxia-driven tumor states, while the spatial relationship between NPY and hypoxia appears weak, heterogeneous, and patient-specific.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 20 matches between paragraphs and lines of code.

itika05/NPY-GBM-transcriptomics

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: caeb79c63c3761f6d273e495044dbb625fc1c43b, 15 August 2026
Languages: R (12)
Size: 18 files, 12 scripts
Software Heritage: not archived
Found in: “4.12. Software and Reproducibility”
Holds: README, license file, CITATION.cff
Not found: environment file, tests, continuous integration, documentation
Tools: tidyverse (10 files), Seurat (5 files), edgeR (4 files), limma (3 files), clusterProfiler (2 files), ggplot2 (2 files), glmnet (2 files), Harmony (2 files), igraph (2 files), metafor (2 files), survival (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
14 files
At the source: github.com/itika05/NPY-GBM-transcriptomics

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • 20 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Data links

Data Availability Statement

No new data were created in this study; all analyses used publicly available datasets. TCGA-GBM and TCGA-LGG RNA-seq and clinical data were obtained from the GDC Data Portal (https://portal.gdc.cancer.gov/, 20 April 2026). GTEx normal brain expression data were obtained from the GTEx Portal (https://gtexportal.org/, 20 April 2026). The microarray, single-cell, and spatial transcriptomic datasets are available from the Gene Expression Omnibus under accession numbers GSE4290, GSE50161, GSE131928, and GSE194329 (https://www.ncbi.nlm.nih.gov/geo/, 20 April 2026). All analysis code used to generate the results, tables, and figures in this study is openly available in a public GitHub repository (https://github.com/itika05/NPY-GBM-transcriptomics, 20 April 2026).

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 10 keywords, 11 MeSH terms, 53 references.

Cite

This paper

Arshad, F., Abualsaud, N., Akbar, A., Khan, M. I., Rasheed, B., Hussain, A., Alghamdi, F. A., Farrash, F. A., Aroke, E. N., Freij, K. W., Arora, I., & Yaqinuddin, A. (2026). Loss of Neuropeptide Y Signaling Accompanies the Neural-to-Mesenchymal Transcriptional Transition in Glioblastoma: A Multi-Scale Transcriptomic Analysis. International journal of molecular sciences, 27(13), 6068. https://doi.org/10.3390/ijms27136068

BibTeX

@article{arshad2026loss,
author = {Arshad, Fareeha and Abualsaud, Nouran and Akbar, Arshiya and Khan, Mohammed Imran and Rasheed, Bushra and Hussain, Adnan and Alghamdi, Fahad Ali and Farrash, Faisal Abdulhameed and Aroke, Edwin N. and Freij, Khalid Walid and Arora, Itika and Yaqinuddin, Ahmed},
title = {{Loss of Neuropeptide Y Signaling Accompanies the Neural-to-Mesenchymal Transcriptional Transition in Glioblastoma: A Multi-Scale Transcriptomic Analysis}},
journal = {International journal of molecular sciences},
year = {2026},
month = jul,
volume = {27},
number = {13},
pages = {6068},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {1422-0067},
doi = {10.3390/ijms27136068},
url = {https://doi.org/10.3390/ijms27136068},
pmid = {42450334},
pmcid = {PMC13361764}
}

RIS

TY - JOUR
AU - Arshad, Fareeha
AU - Abualsaud, Nouran
AU - Akbar, Arshiya
AU - Khan, Mohammed Imran
AU - Rasheed, Bushra
AU - Hussain, Adnan
AU - Alghamdi, Fahad Ali
AU - Farrash, Faisal Abdulhameed
AU - Aroke, Edwin N.
AU - Freij, Khalid Walid
AU - Arora, Itika
AU - Yaqinuddin, Ahmed
TI - Loss of Neuropeptide Y Signaling Accompanies the Neural-to-Mesenchymal Transcriptional Transition in Glioblastoma: A Multi-Scale Transcriptomic Analysis
T2 - International journal of molecular sciences
J2 - Int J Mol Sci
PY - 2026
DA - 2026/07/06
VL - 27
IS - 13
SP - 6068
SN - 1422-0067
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/ijms27136068
UR - https://doi.org/10.3390/ijms27136068
LA - en
ER -

CSL-JSON

{
"id": "10.3390/ijms27136068",
"type": "article-journal",
"title": "Loss of Neuropeptide Y Signaling Accompanies the Neural-to-Mesenchymal Transcriptional Transition in Glioblastoma: A Multi-Scale Transcriptomic Analysis",
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{
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[9] doi:10.1038/s41593-026-02367-0 [code]
A reproducible three-dimensional model of human brain tissue to investigate physiological and disease-associated microglia phenotypes.
Journal: Nature neuroscience
In common: Harmony, edgeR, limma, 5 other tools, cellular / molecular, 3 references
[10] doi:10.1038/s42003-026-10957-8 [code]
Brain defence by the extracellular matrix protein Cochlin.
Journal: Communications biology
In common: glmnet, survival, edgeR, 6 other tools, cellular / molecular

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