OSCR

Pericyte-derived cancer-associated fibroblasts correlate with poor survival and are enriched after chemoradiotherapy in glioblastoma.

Code ↔ Paper

5 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 5 matches
  1. [1] § Materials and methods › Bulk RNA-seq analysis and CAF quantification ↔ gse190504_cellanneal_deconvolution.py, lines 200–290 · score 0.98 · bulk_max, bulk_min, disp_min, IDH1 mut, IDH1 wt, CAF proportion
  2. [2] § Materials and methods › Bulk RNA-seq analysis and CAF quantification ↔ gse190504_CAF_rank_score.py, lines 306–437 · score 0.91 · CAF rank score, IDH1 mut, IDH1 wt, BH adjusted, Wilcoxon, CAF signature
  3. [3] § Materials and methods › Single cell analysis ↔ functions.R, lines 616–653 · score 0.72 · decontX, contamination, copy, Seurat, single cell, neighbor
  4. [4] § Materials and methods › Single cell analysis ↔ load_data.R, lines 114–147 · score 0.53 · algorithm, GSE103224, GSM3828672, GSE135045, Harmony, neighbor
  5. [5] § Results › CAF transcriptional signature reveals association with poor prognosis and therapy-related expansion ↔ gse190504_cellanneal_deconvolution.py, lines 200–290 · score 0.52 · CAF proportions, untreated, Spearman, histology, IDH1, bulk

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 294 lines · 11 KB · no license · 2 matches

  1. from __future__ import annotations
  3. import re
  4. import sys
  5. import zipfile
  6. import xml.etree.ElementTree as ET
  7. from pathlib import Path
  9. import numpy as np
  10. import pandas as pd
  11. from scipy.stats import mannwhitneyu, spearmanr, pearsonr
  13. # ---------------------------------------------------------------------------
  14. # paths
  15. ROOT = Path(__file__).resolve().parent
  16. BASE = ROOT / "GSE190504"
  18. EXPR_XLSX = BASE / "GSE190504_Processed_Data_Spreadsheet_Glioma_Study.xlsx"
  19. META_TXT = BASE / "GSE190504_series_matrix.txt"
  20. REF_CSV = ROOT / "bulk_ref" / "reference_celltype_mean.csv"
  22. OUT_DIR = BASE / "glio_deconv_bulkref_cellanneal"
  23. OUT_DIR.mkdir(parents=True, exist_ok=True)
  24. OUT_PROP = OUT_DIR / "GSE190504_glio_cellanneal_proportions.csv"
  25. OUT_STATS = OUT_DIR / "GSE190504_glio_cellanneal_stats.txt"
  27. CELLANNEAL_REPO = ROOT / "cellanneal_repo"
  28. sys.path.insert(0, str(CELLANNEAL_REPO))
  30. from scipy.optimize import dual_annealing as _scipy_dual_annealing
  31. from cellanneal import general as _ca_general
  32. _ca_general.dual_annealing = _scipy_dual_annealing
  34. from cellanneal.pipelines import run_cellanneal
  37. # cellanneal hyper-parameter
  38. DISP_MIN = 0.5
  39. BULK_MIN = 1e-8
  40. BULK_MAX = 1.0
  41. MAXITER = 500
  43. # ---------------------------------------------------------------------------
  44. # self-contained XLSX reader (avoids the openpyxl dependency)
  45. # ---------------------------------------------------------------------------
  46. _NS = {"s": "http://schemas.openxmlformats.org/spreadsheetml/2006/main"}
  49. def _col_letters_to_index(letters: str) -> int:
  50. idx = 0
  51. for ch in letters:
  52. idx = idx * 26 + (ord(ch.upper()) - ord("A") + 1)
  53. return idx - 1
  56. def read_xlsx_sheet(xlsx_path: Path, sheet_index: int = 0) -> list[list[str | None]]:
  57. with zipfile.ZipFile(xlsx_path) as zf:
  58. shared: list[str] = []
  59. if "xl/sharedStrings.xml" in zf.namelist():
  60. tree = ET.parse(zf.open("xl/sharedStrings.xml"))
  61. for si in tree.getroot().findall("s:si", _NS):
  62. pieces = [t.text or "" for t in si.iter("{%s}t" % _NS["s"])]
  63. shared.append("".join(pieces))
  65. sheet_names = sorted(
  66. n for n in zf.namelist() if n.startswith("xl/worksheets/sheet")
  67. )
  68. tree = ET.parse(zf.open(sheet_names[sheet_index]))
  69. rows = tree.getroot().find("s:sheetData", _NS)
  71. out: list[list[str | None]] = []
  72. max_cols = 0
  73. for row in rows.findall("s:row", _NS):
  74. cells: dict[int, str | None] = {}
  75. for c in row.findall("s:c", _NS):
  76. col_letters = re.match(r"[A-Z]+", c.attrib["r"]).group(0)
  77. col_idx = _col_letters_to_index(col_letters)
  78. t = c.attrib.get("t", "n")
  79. v_node = c.find("s:v", _NS)
  80. if t == "s":
  81. cells[col_idx] = shared[int(v_node.text)] if v_node is not None else None
  82. elif t == "inlineStr":
  83. is_node = c.find("s:is", _NS)
  84. txt = "".join((tn.text or "") for tn in (is_node.iter("{%s}t" % _NS["s"]) if is_node is not None else []))
  85. cells[col_idx] = txt
  86. else:
  87. cells[col_idx] = v_node.text if v_node is not None else None
  88. if cells:
  89. max_cols = max(max_cols, max(cells) + 1)
  90. out.append([cells.get(i) for i in range(max_cols)])
  91. for r in out:
  92. if len(r) < max_cols:
  93. r.extend([None] * (max_cols - len(r)))
  94. return out
  97. # ---------------------------------------------------------------------------
  98. # helpers
  99. # ---------------------------------------------------------------------------
  100. def fmt_p(p: float) -> str:
  101. if p is None or (isinstance(p, float) and (np.isnan(p) or np.isinf(p))):
  102. return "NA"
  103. if p < 1e-4:
  104. return f"{p:.4e}"
  105. return f"{p:.4f}"
  108. def sanitize_ct(name: str) -> str:
  109. return re.sub(r"[^A-Za-z0-9]+", "_", str(name))
  112. def clean_chr(values) -> list[str]:
  113. out = []
  114. for v in values:
  115. s = "" if v is None else str(v)
  116. s = s.strip()
  117. s = re.sub(r'^"|"$', "", s)
  118. out.append(s.strip())
  119. return out
  122. def parse_series_metadata(path: Path) -> pd.DataFrame:
  123. lines = path.read_text(errors="replace").splitlines()
  125. def get_row(prefix: str) -> list[str]:
  126. for ln in lines:
  127. if ln.startswith(prefix):
  128. return clean_chr(ln.split("\t")[1:])
  129. return []
  131. def get_rows(prefix: str) -> list[list[str]]:
  132. return [clean_chr(ln.split("\t")[1:]) for ln in lines if ln.startswith(prefix)]
  134. titles = get_row("!Sample_title")
  135. chars = get_rows("!Sample_characteristics_ch1")
  136. if len(chars) < 5:
  137. raise RuntimeError("Expected >=5 !Sample_characteristics_ch1 lines.")
  139. histology = [re.sub(r"^histology:\s*", "", v) for v in chars[2]]
  140. genotype = [re.sub(r"^genotype:\s*", "", v) for v in chars[3]]
  141. treatment = [re.sub(r"^treatment:\s*", "", v) for v in chars[4]]
  143. meta = pd.DataFrame({
  144. "sample_id": titles,
  145. "histology": histology,
  146. "genotype": genotype,
  147. "treatment": treatment,
  148. })
  149. meta["idh1"] = np.where(meta["genotype"].str.contains("IDH1-mut", regex=False), "IDH1-mut", "IDH1-wt")
  150. meta["treatment_simple"] = np.where(meta["treatment"] == "Untreated", "Untreated", "Treated")
  151. return meta
  154. def load_expression(xlsx_path: Path) -> pd.DataFrame:
  155. """Return gene x sample DataFrame, gene symbols as index, linear scale."""
  156. sheet = read_xlsx_sheet(xlsx_path, sheet_index=0)
  157. if not sheet:
  158. raise RuntimeError("XLSX appears empty")
  160. ensg = [s if s is not None else "" for s in sheet[0][2:]]
  161. sym = [(s.upper() if s is not None else "") for s in sheet[1][2:]]
  162. for k in range(len(sym)):
  163. if not sym[k]:
  164. sym[k] = ensg[k].upper()
  166. data_rows = sheet[5:]
  167. sample_title = [r[1] if len(r) > 1 else None for r in data_rows]
  168. sample_title = [s if s is not None else "" for s in sample_title]
  170. values = np.full((len(data_rows), len(sym)), np.nan, dtype=float)
  171. for i, r in enumerate(data_rows):
  172. for j in range(len(sym)):
  173. v = r[2 + j] if 2 + j < len(r) else None
  174. if v is None or v == "":
  175. continue
  176. try:
  177. values[i, j] = float(v)
  178. except ValueError:
  179. continue
  180. values = np.nan_to_num(values, nan=0.0)
  182. expr = pd.DataFrame(values.T, index=pd.Index(sym, name="symbol"), columns=sample_title)
  183. expr.index = expr.index.astype(str).str.upper().str.replace(r"\..*$", "", regex=True)
  184. expr = expr.loc[(expr.index != "") & (~expr.index.isna())]
  185. if expr.index.duplicated().any():
  186. expr = expr.groupby(level=0, sort=False).sum()
  187. return expr
  190. def load_reference(ref_csv: Path) -> pd.DataFrame:
  191. ref = pd.read_csv(ref_csv, index_col=0)
  192. ref.index = ref.index.astype(str).str.upper().str.replace(r"\..*$", "", regex=True)
  193. ref = ref.loc[(ref.index != "") & (~ref.index.isna())]
  194. ref = ref[~ref.index.duplicated(keep="first")]
  195. ref.columns = [sanitize_ct(c) for c in ref.columns]
  196. ref = ref.apply(pd.to_numeric, errors="coerce").fillna(0.0)
  197. return ref
  200. # ---------------------------------------------------------------------------
  201. # main
  202. # ---------------------------------------------------------------------------
  203. def main() -> None:
  204. print(f"[info] reading expression: {EXPR_XLSX}")
  205. expr = load_expression(EXPR_XLSX)
  206. print(f"[info] expression matrix: {expr.shape[0]} genes x {expr.shape[1]} samples")
  208. print(f"[info] reading metadata: {META_TXT}")
  209. meta = parse_series_metadata(META_TXT)
  211. glio_samples = [s for s in expr.columns if s in set(meta.loc[meta["histology"] == "Glio", "sample_id"])]
  212. bulk = expr.loc[:, glio_samples].copy()
  213. print(f"[info] Glio samples: {bulk.shape[1]}")
  215. print(f"[info] reading reference: {REF_CSV}")
  216. ref = load_reference(REF_CSV)
  217. print(f"[info] reference: {ref.shape[0]} genes x {ref.shape[1]} cell types")
  219. common = bulk.index.intersection(ref.index)
  220. print(f"[info] common genes (raw): {len(common)}")
  221. bulk2 = bulk.loc[common].copy()
  222. ref2 = ref.loc[common].copy()
  224. keep = (bulk2.sum(axis=1) > 0) & (ref2.sum(axis=1) > 0)
  225. bulk2 = bulk2.loc[keep]
  226. ref2 = ref2.loc[keep]
  227. print(f"[info] common genes after non-zero filter: {bulk2.shape[0]}")
  228. if bulk2.shape[0] < 1000:
  229. raise RuntimeError("Too few common genes after filtering; aborting.")
  231. # composition normalisation per sample / per cell type, matching the
  232. # GSE222515 cellanneal_bulkref pipeline.
  233. bulk2 = bulk2.div(bulk2.sum(axis=0), axis=1).fillna(0.0)
  234. ref2 = ref2.div(ref2.sum(axis=0), axis=1).fillna(0.0)
  236. print(f"[info] running cellanneal (maxiter={MAXITER}) ...")
  237. mix = run_cellanneal(
  238. celltype_df=ref2,
  239. bulk_df=bulk2,
  240. disp_min=DISP_MIN,
  241. bulk_min=BULK_MIN,
  242. bulk_max=BULK_MAX,
  243. maxiter=MAXITER,
  244. )
  246. res = mix.copy().reset_index().rename(columns={"index": "sample_id"})
  247. # standardise CAF column name
  248. caf_col = next((c for c in res.columns if c.lower() == "cafs"), None)
  249. if caf_col is None:
  250. raise RuntimeError("CAFs column not found in cellanneal output")
  251. if caf_col != "CAFs":
  252. res = res.rename(columns={caf_col: "CAFs"})
  254. res = res.merge(meta[["sample_id", "idh1", "treatment_simple", "histology"]], on="sample_id", how="left")
  255. res.to_csv(OUT_PROP, index=False)
  256. print(f"[saved] {OUT_PROP}")
  258. # ---- statistics
  259. caf_wt = res.loc[res["idh1"] == "IDH1-wt", "CAFs"].to_numpy()
  260. caf_mut = res.loc[res["idh1"] == "IDH1-mut", "CAFs"].to_numpy()
  261. p_idh = mannwhitneyu(caf_wt, caf_mut, alternative="two-sided").pvalue if len(caf_wt) and len(caf_mut) else np.nan
  263. caf_unt = res.loc[res["treatment_simple"] == "Untreated", "CAFs"].to_numpy()
  264. caf_trt = res.loc[res["treatment_simple"] == "Treated", "CAFs"].to_numpy()
  265. p_trt = mannwhitneyu(caf_unt, caf_trt, alternative="two-sided").pvalue if len(caf_unt) and len(caf_trt) else np.nan
  267. # report cellanneal fit QC if present
  268. qc_cols = [c for c in res.columns if c.lower() in {"rho_spearman", "rho_pearson"}]
  269. qc_summary = ""
  270. if qc_cols:
  271. rho_s = res["rho_Spearman"].median() if "rho_Spearman" in res.columns else np.nan
  272. rho_p = res["rho_Pearson"].median() if "rho_Pearson" in res.columns else np.nan
  273. qc_summary = f"Median rho_Spearman: {rho_s:.4f} | Median rho_Pearson: {rho_p:.4f}"
  275. lines = [
  276. f"Run: {pd.Timestamp.now()}",
  277. f"Glio samples: {len(res)}",
  278. f"Common genes used: {len(common)} (after nonzero filter: {bulk2.shape[0]})",
  279. f"Reference cell types: {', '.join(ref2.columns)}",
  280. f"Median CAF proportion: {res['CAFs'].median():.6f}",
  281. f"Nonzero CAF count: {int((res['CAFs'] > 0).sum())}",
  282. f"IDH1 wt vs mut p (MWU): {fmt_p(p_idh)} (n_wt={len(caf_wt)}, n_mut={len(caf_mut)})",
  283. f"Untreated vs Treated p (MWU): {fmt_p(p_trt)} (n_unt={len(caf_unt)}, n_trt={len(caf_trt)})",
  284. ]
  285. if qc_summary:
  286. lines.append(qc_summary)
  288. OUT_STATS.write_text("\n".join(lines) + "\n")
  289. print(f"[saved] {OUT_STATS}")
  290. print("\n".join(lines))
  293. if __name__ == "__main__":
  294. main()

gse190504_cellanneal_deconvolution.py at commit d0ffbb4, no license · at the source

Overview

Authors: Aly Ismailov1, Maria Poptsova1
ORCID iDs: Aly Ismailov, Maria Poptsova
  1. Centre for Biomedical Research and Technologies, Institute of Artificial Intelligence and Digital Sciences, Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia
Institutions: National Research University Higher School of Economics (Russia)
Journal: PloS one, volume 21, issue 9, article e0355902
Dates: received 5 June 2026; accepted 29 July 2026; published online 10 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0355902 · PMID 42721145 · PMCID PMC13561374 · OpenAlex W7212124242
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, Preprocessing, Connectivity
MeSH: Brain Neoplasms*, Cancer-Associated Fibroblasts*, Chemoradiotherapy*, Glioblastoma*, Pericytes*, Gene Expression Regulation, Neoplastic, Humans, Tumor Microenvironment (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: HSE University (HSE-BR-2025-21)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

The role of cancer-associated fibroblasts (CAFs) in glioblastoma remains unclear, as their existence in the brain tumor microenvironment is still debated, given that the normal brain parenchyma is devoid of fibroblasts. It is unclear whether cells described as CAFs represent a distinct stromal population or a transcriptional state of perivascular cells such as pericytes. The aim of this study was to determine the identity, origin, and functional relevance of CAFs in glioblastoma. We analyzed 54 single-cell RNA sequencing datasets together with 88 bulk RNA sequencing samples. We identified a continuous transcriptional spectrum linking endothelial cells, pericytes, and CAFs, supporting pericytes as the most likely source of CAFs in glioblastoma. We further derived and validated robust CAF- and pericyte-specific gene signatures, enabling clear separation of these populations across cohorts. Reproducible CAF-associated ligand–receptor interactions were enriched in angiogenesis and immune modulation pathways. In bulk RNA-seq data, both CAF signature scoring and deconvolution consistently demonstrated increased CAF abundance in IDH-wildtype gliomas and further enrichment after chemoradiotherapy, while selective CYP1B1 expression in CAFs suggested a potential association with therapy-induced tumor adaptation. Overall, CAFs represent a distinct, pericyte-related stromal population in glioblastoma with conserved transcriptional and signaling programs. High CAF signature scores were associated with poorer overall and progression-free survival and were enriched in IDH-wildtype and post-chemoradiotherapy gliomas, suggesting a role for CAFs in therapy-associated remodeling of the tumor microenvironment in aggressive disease.

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 5 matches between paragraphs and lines of code.

neuropromotion/CAFs-in-glioblastoma-microenvironment

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: d0ffbb4fd540b418b830368d456905d97cfc059d, 11 September 2026
Languages: R (9), Python (2)
Size: 18 files, 11 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), ComplexHeatmap (4 files), Seurat (4 files), circlize (3 files), ggplot2 (3 files), Harmony (2 files), NumPy (2 files), pandas (2 files), Plotly (2 files), SciPy (2 files), data.table (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
12 files
At the source: github.com/neuropromotion/CAFs-in-glioblastoma-microenvironment

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;
  • 11 scripts, each with its path and the digest of its content;
  • 5 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

Datasets cited

Data Availability

All code used for data processing, analysis, figure generation, and 3D diffusion map visualization is publicly available at: https://github.com/neuropromotion/CAFs-in-glioblastoma-microenvironment.

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, 2 authors, 8 MeSH terms, 1 funder, 50 references.

Cite

This paper

Ismailov, A., & Poptsova, M. (2026). Pericyte-derived cancer-associated fibroblasts correlate with poor survival and are enriched after chemoradiotherapy in glioblastoma. PloS one, 21(9), e0355902. https://doi.org/10.1371/journal.pone.0355902

BibTeX

@article{ismailov2026pericyte,
author = {Ismailov, Aly and Poptsova, Maria},
title = {{Pericyte-derived cancer-associated fibroblasts correlate with poor survival and are enriched after chemoradiotherapy in glioblastoma}},
journal = {PloS one},
year = {2026},
month = sep,
volume = {21},
number = {9},
pages = {e0355902},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0355902},
url = {https://doi.org/10.1371/journal.pone.0355902},
pmid = {42721145},
pmcid = {PMC13561374}
}

RIS

TY - JOUR
AU - Ismailov, Aly
AU - Poptsova, Maria
TI - Pericyte-derived cancer-associated fibroblasts correlate with poor survival and are enriched after chemoradiotherapy in glioblastoma
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/09/10
VL - 21
IS - 9
SP - e0355902
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0355902
UR - https://doi.org/10.1371/journal.pone.0355902
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pone.0355902",
"type": "article-journal",
"title": "Pericyte-derived cancer-associated fibroblasts correlate with poor survival and are enriched after chemoradiotherapy in glioblastoma",
"container-title": "PloS one",
"author": [
{
"family": "Ismailov",
"given": "Aly"
},
{
"family": "Poptsova",
"given": "Maria"
}
],
"container-title-short": "PLoS One",
"volume": "21",
"issue": "9",
"page": "e0355902",
"DOI": "10.1371/journal.pone.0355902",
"PMID": "42721145",
"PMCID": "PMC13561374",
"ISSN": "1932-6203",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pone.0355902",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
10
]
]
}
}

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In common: Harmony, circlize, ComplexHeatmap, 8 other tools
[10] doi:10.1186/s13073-026-01704-z [code]
Gene expression profiling enables refined parcellation of cortical layers in the heterogeneous human cerebral cortex.
Journal: Genome medicine
In common: Harmony, SingleCellExperiment, ComplexHeatmap, 7 other tools, genetics / omics, cellular / molecular

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