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REVIEW 3 major objections 7 minor 32 references

MAPK Pathway Activity and Heme Biosynthesis Gene Expression in IDH-Wildtype Glioblastoma: A Purity-Adjusted, Discovery-Validation Analysis

T0 review · 3 major / 7 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read In IDH-wildtype glioblastoma, MAPK pathway activity is reproducibly associated with lower expression of PPOX, the enzyme that synthesizes the fluorescent molecule protoporphyrin IX, in two independent, purity-adjusted cohorts.

desk verdict A clean replicated correlational result that usefully relocates the MAPK–PpIX link from clearance to synthesis, but the mechanistic framing outruns the protein-level evidence. read the letter →

arxiv 2608.07865 v1 pith:S4KQBNNO submitted 2026-08-08 q-bio.GN q-bio.MNq-bio.QM

Manveer Singh Tib This is my paper · ORCID
classification q-bio.GNq-bio.MNq-bio.QM
keywords Glioblastoma5-aminolevulinicacidprotoporphyrinIXfluorescence-guidedsurgeryMAPKsignalingpathwayhemebiosynthesisprotoporphyrinogenoxidasetumorpurityadjustment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Using two independent, purity-adjusted sets of primary IDH-wildtype glioblastoma specimens, this paper tests whether the MAPK signaling pathway controls the fluorescent molecule protoporphyrin IX (PpIX) in human tissue the way cell-culture studies say it does. It finds that the two clearance-related effectors proposed by the cell-line model, ABCB1 and FECH, do not reproduce across both cohorts. Instead, MAPK pathway activity is consistently associated with lower expression of PPOX, the enzyme that makes PpIX in the final synthetic step, in both cohorts. The paper reads this as relocating the likely regulatory point from PpIX removal to PpIX synthesis, and as identifying MEK/MAPK inhibition as a candidate lever for making 5-ALA-guided surgery fluorescence more reliably. This matters because the prior mechanism had never been confirmed in whole human tumors.

What carries the argument

The central object is PPOX, protoporphyrinogen oxidase, the enzyme that oxidizes protoporphyrinogen IX into the fluorescent PpIX; its transcript abundance is the outcome variable. The pathway instrument is a ten-gene transcriptional activity score averaged per cohort (its constituent targets: DUSP4, DUSP6, ETV4, ETV5, PHLDA1, SPRY2, SPRY4, CCND1, EPHA2, EPHA4), and the load-bearing statistic is the purity-adjusted partial rank correlation, with false-discovery-rate correction applied across the gene panel within each cohort. Replication is defined prospectively as reaching the adjusted threshold in both cohorts with the same direction; only PPOX meets it. Because the two expression matrices are analyzed separately, the cross-cohort agreement is the validation.

What would settle it

Measure PpIX fluorescence or PpIX abundance directly in IDH-wildtype glioblastoma samples stratified by MAPK activity and PPOX expression. If high-MAPK tumors with low PPOX messenger RNA do not show lower PpIX than low-MAPK tumors, the claimed synthesis-locus mechanism fails; similarly, if manipulating PPOX expression changes messenger RNA but not PpIX production, the transcript-to-function bridge breaks.

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Extended reading notes

Core claim

The central discovery, on the paper's own terms, is a reproducible inverse association between MAPK pathway activity and PPOX transcript expression in IDH-wildtype glioblastoma. The association appears in both cohorts after purity adjustment: discovery rho = -0.40 with bootstrap 95% CI [-0.54,-0.25] and adjusted p < 0.001; validation rho = -0.28 with 95% CI [-0.47,-0.07] and adjusted p = 0.0342. It survives permutation testing, bootstrap resampling, alternative purity-adjustment schemes, and re-scoring of the pathway activity measure with each of its ten constituent genes removed one at a time. The effectors the cell-line model predicted, ABCB1 and FECH, do not hold to the same replicated standard, with FECH significant in one cohort only and ABCB1 in neither. The paper concludes that if the correlation is causal, MAPK controls PpIX at the synthetic step through PPOX, and that this relationship is biological rather than prognostic.

Load-bearing premise

The central assumption is that PPOX messenger-RNA abundance in bulk tumor tissue, after adjusting for tumor purity, faithfully reflects how much functional PPOX enzyme the tumor cells make, and therefore how much fluorescent PpIX they can synthesize.

Editorial extensions

If this is right

  • If the correlation is causal, suppressing MAPK signaling becomes a testable route to raise 5-ALA fluorescence, because the reproducible control point is the synthesis step rather than the efflux or conversion step.
  • The cell-line-predicted effectors ABCB1 and FECH do not meet the same replication standard, so candidate lists from cell culture should be narrowed before clinical targeting.
  • The absence of a survival association means MAPK pathway status is not supported as a prognostic marker; its relevance, if any, is to fluorescence rather than outcome.
  • The association's robustness to leaving out each of the ten score genes one at a time and to alternative purity-adjustment schemes indicates it is not an artifact of a single pathway gene or a particular purity metric.
  • Since PPOX expression does not differ across transcriptional subtypes while MAPK activity does, the continuous MAPK-PPOX association is not a byproduct of subtype grouping.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • I infer the decisive untested experiment is direct measurement of PpIX accumulation in tumors stratified by MAPK activity and PPOX expression; without it, lower PPOX transcript could reflect a broad anti-biosynthetic program rather than a specific control of fluorescence.
  • The protein-level result in the paper's exploratory cohort ran in the same direction but did not reach significance, so a larger matched proteomic study would test whether the transcript-level signal survives translation to functional enzyme.
  • A natural follow-up is to ask whether ETS-family or AP-1 transcription factors, the usual MAPK effectors, bind and regulate the PPOX promoter; the paper leaves that open.
  • Because another heme-pathway gene showed an equally strong correlation in the discovery cohort, a genome-wide correlation screen would test whether the PPOX association is specific to heme synthesis or one instance of MAPK anti-correlating broadly with metabolic transcription.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 7 minor

Summary. The manuscript tests, in two independent IDH-wildtype glioblastoma cohorts (TCGA-GBM, n=140; CGGA, n=87), whether MAPK/ERK pathway activity—quantified by the externally developed MPAS transcriptional score and cross-checked with ssGSEA of HALLMARK_KRAS_SIGNALING_UP—correlates with expression of heme-biosynthesis and PpIX-processing genes, with tumor purity as a covariate. Against the cell-line hypothesis that MAPK upregulates ABCB1 and FECH, neither effector replicated across cohorts (ABCB1 null in both; FECH positive only in CGGA, rho=0.28, q=0.0342). The only gene meeting the stated replication criterion (concordant direction, BH q<0.05 in both cohorts) was PPOX: purity-adjusted partial Spearman rho=-0.40 (95% CI [-0.54,-0.25], q<0.001) in TCGA and rho=-0.28 (95% CI [-0.47,-0.07], q=0.0342) in CGGA. Robustness checks include bootstrap CIs, permutation tests, alternative purity metrics, and leave-one-gene-out MPAS recalculation. The authors interpret the finding as relocating the MAPK–PpIX association from clearance to synthesis, note that the CPTAC protein-level check is directionally consistent but non-significant (rho=-0.13, p=0.21), and propose MAPK/MEK inhibition as a candidate handle for increasing 5-ALA fluorescence.

Significance. If the correlational result is taken at face value, this is a useful, well-controlled replicated observation in a clinically relevant setting. The statistical design is strong: independent cohorts, a symmetric replication criterion, bootstrap and permutation checks, purity adjustment, an orthogonal pathway score, and explicit reporting of non-replicating genes. No fitted parameters are introduced, and the authors are unusually candid about limitations. The main reservation is that the translational/mechanistic label ('handle for increasing 5-ALA fluorescence') goes beyond the transcript-level evidence; the CPTAC protein result is non-significant, and the paper itself cites evidence that fluorescence follows PPOX protein rather than mRNA. As a hypothesis-generating finding with clear functional next steps, the paper is valuable; as a mechanistic conclusion, it needs tempering or additional protein/functional validation.

major comments (3)
  1. [Abstract / Section 7] The Abstract's closing claim that the analysis identifies the MAPK pathway as 'a candidate handle for increasing 5-ALA fluorescence' and Section 7's statement that the results 'support the hypothesis that increased MAPK activity lowers tumor fluorescence' go beyond what the transcript-level data can establish. Section 6.4 reports that the CPTAC-GBM protein-level correlation is non-significant (rho=-0.13, 95% CI [-0.32,0.07], p=0.21) and cites Ref. [9] showing that fluorescence tracks PPOX protein rather than mRNA. Since the translational handle depends on transcript-to-protein-to-function propagation, the Abstract and Section 7 should either present this as a hypothesis requiring functional validation or remove the claim.
  2. [Section 5.3] The ssGSEA cross-check is presented as an independent confirmation, but the text does not state whether these correlations are purity-adjusted partial Spearman statistics (as in the primary analysis) or unadjusted Spearman correlations. If unadjusted, the cross-check does not address the primary purity-adjusted claim; if adjusted, the method should be stated. Please clarify and, if necessary, report the purity-adjusted values.
  3. [Section 6.1] The discussion acknowledges that the PPOX association is compatible with MPAS being broadly anti-correlated with metabolic and biosynthetic transcription and that a genome-wide comparison or proliferation-signature adjustment is needed to establish specificity. This is a load-bearing caveat for the paper's framing of PPOX as the uniquely replicated node and for the mechanistic relocation to synthesis. The manuscript should either add such an analysis or explicitly restrict the conclusion to the pre-specified heme panel and state in the Abstract that specificity beyond the panel is not established.
minor comments (7)
  1. [Abstract] There is a typo in the Abstract: 'withPPOX' should be 'with PPOX'.
  2. [Reference [22]] The software name in Reference [22] is printed as 'GSV A'; it should be 'GSVA'.
  3. [Section 5.4] The sentence reporting 'all p <0.05 after Benjamini–Hochberg correction' across the seven purity-adjustment scenarios is ambiguous: please clarify whether the correction is applied within each scenario or across the scenario tests, and report the corrected values consistently.
  4. [Section 6.4] Please state explicitly whether the CPTAC-GBM analysis uses MPAS computed from matched RNA-seq and PPOX protein from mass spectrometry, and whether the reported rho is an unadjusted or purity-adjusted partial correlation; the current text calls it an 'unadjusted proteomic analysis' but does not define the statistic.
  5. [Code and Data Availability] The statement that code and results are 'available from the author upon reasonable request' is not a reproducible-availability standard; please deposit the code and processed data in a public repository with a persistent identifier.
  6. [Section 3.2] The quality-control exclusion based on RN7SL2 accounting for more than 20% of reads is not cited or justified; please provide a reference or a rationale for this threshold.
  7. [Section 5.5] The Neural subtype has n=6 and is described as underpowered in the text; please include this caveat in the Figure 4 caption as well, so the figure is not read without the sample-size warning.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: MPAS and PPOX are independent, externally sourced measurements, and the reported association is an empirical result rather than an input to the analysis.

full rationale

The paper's central claim is that MAPK pathway activity, quantified by the externally developed MPAS transcriptional signature (Wagle et al., reference [19]), is reproducibly negatively associated with PPOX expression in two independent IDH-wildtype glioblastoma cohorts. MPAS is defined from ten MAPK/ERK transcriptional targets (DUSP4, DUSP6, ETV4, ETV5, PHLDA1, SPRY2, SPRY4, CCND1, EPHA2, EPHA4); PPOX is not among them and is not used to construct the score. The alternative pathway metric uses ssGSEA on the HALLMARK_KRAS_SIGNALING_UP gene set from MSigDB, another externally curated set. PPOX expression is taken directly from TCGA and CGGA RNA-seq matrices, and the analysis uses partial Spearman correlation with tumor purity as a covariate. No parameter is fitted to the PPOX data and then reported as a prediction; the correlation is the measured outcome. The replication criterion (BH-adjusted p < 0.05 with concordant direction in both cohorts) is stated before presenting the results and is applied symmetrically; the fact that PPOX is the only gene meeting it is an empirical contingency, not a definitional consequence. The paper explicitly acknowledges the main limitations in Section 6.4: bulk transcript abundance may not reflect protein-level PPOX function, the CPTAC proteomic replication was not significant, and PpIX was not directly measured. These are validity concerns, not circularity. There are no load-bearing self-citations: the author cites no prior work of their own, and the pathway signatures and statistical methods come from independent, published sources. The derivation chain is therefore self-contained: an external pathway score is correlated with an independently measured gene, with transparent adjustment, correction, and replication. No circular step can be exhibited.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no free parameters (MPAS weights are fixed from prior literature) and no invented entities. Its central claim rests on standard statistical tools and on domain assumptions about the validity of the pathway score, purity estimates, transcript-to-protein correspondence, and cohort classification.

assumptions (5)
  • domain assumption MPAS (average z-score of ten MAPK transcriptional targets) is a valid measure of MAPK/ERK pathway activity in glioblastoma tissue.
    Invoked in Section 4.1; the signature comes from Wagle et al. and is cross-checked with ssGSEA, but it is not independently validated on protein-level MAPK activity in these GBM cohorts.
  • domain assumption Tumor purity estimates (ABSOLUTE for TCGA, ESTIMATE for CGGA) are accurate enough that partial Spearman correlations remove stromal and immune contamination.
    Section 4.3; the two cohorts use different estimators, and a same-metric control (ESTIMATE for both) is provided, but residual bias in either estimator would affect the adjusted correlations.
  • domain assumption PPOX transcript abundance is a valid proxy for functional PPOX enzyme activity controlling PpIX synthesis.
    Section 6.4; the CPTAC proteomic check was directionally consistent but not significant, so the mechanistic relocation to synthesis depends on this unconfirmed transcript-to-function link.
  • domain assumption IDH-wildtype primary GBM classification from the cited annotation sources is correct for both cohorts.
    Section 3.1; misclassification would add noise or bias to the correlations.
  • domain assumption No direct PpIX measurement is needed for the reported correlation; the biological interpretation is conditional on future functional studies.
    The paper explicitly states in Sections 6.1 and 6.4 that causality and PpIX accumulation cannot be established from gene-expression data alone.

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Cite this review

Pith. "Pith review of MAPK Pathway Activity and Heme Biosynthesis Gene Expression in IDH-Wildtype Glioblastoma: A Purity-Adjusted, Discovery-Validation Analysis." pith.science (2026). https://pith.science/paper/S4KQBNNO

@misc{pith2026260807865,
  author       = {Pith},
  title        = {Pith review of: MAPK Pathway Activity and Heme Biosynthesis Gene Expression in IDH-Wildtype Glioblastoma: A Purity-Adjusted, Discovery-Validation Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S4KQBNNO}},
  note         = {Machine review of arXiv:2608.07865}
}
read the original abstract

5-aminolevulinic acid (5-ALA) promotes fluorescence-based resection of glioblastoma via protoporphyrin IX (PpIX); however, there is considerable heterogeneity in fluorescence intensity, limiting margin distinction. Cell-line studies show that stimulation of the MAPK pathway results in decreased PpIX levels through increased elimination by ABCB1, an efflux transporter, and ferrochelatase (FECH), the enzyme that converts PpIX to heme. However, the mechanism has yet to be studied in human tissue. Using two independent, purity-adjusted sets of primary IDH-wildtype glioblastoma specimens (TCGA-GBM, n=140, discovery; CGGA, n=87, validation), no reproducible correlation between MAPK signaling and its proposed downstream effectors (ABCB1 and FECH) could be detected. Instead, MAPK activity displayed a reproducibly negative relationship with PPOX, the enzyme that converts protoporphyrinogen IX to protoporphyrin IX: TCGA-GBM (n=133 with purity estimates; rho = -0.40, 95% bootstrap CI [-0.54, -0.25], adjusted p < 0.001), CGGA (rho = -0.28, 95% bootstrap CI [-0.47, -0.07], adjusted p = 0.0342). The finding was robust to permutation testing and to alternative approaches to purity adjustment. Here we report, for the first time, replicated human-tissue evidence for this association. If this correlation reflects causality, MAPK regulation of PpIX would act at the point of synthesis rather than at the clearance steps implied by previous cell-culture studies. We observed differential activation of the MAPK pathway with increased activation in the classical subtype and decreased activation in the proneural subtype. There was no correlation between MAPK pathway activation and overall survival (log-rank p = 0.73). The association between PPOX expression and MAPK pathway activation is biological rather than prognostic, identifying the MAPK pathway as a candidate handle for increasing 5-ALA fluorescence.

Figures

Figures reproduced from arXiv: 2608.07865 by the authors.

Figure 1
Figure 1. Heme biosynthesis and the hypothesized points of MAPK regulation. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Purity-adjusted partial Spearman associations between MPAS and the predicted effectors [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Purity-adjusted associations between MAPK pathway activity and heme-pathway gene expression. Points denote the purity-adjusted partial Spearman ρ between MAPK pathway activity score (MPAS) and gene expression; horizontal bars denote 95% confidence intervals obtained by Fisher z-transformation. Partial Spearman correlations were computed with tumor purity as the controlling variable. Tumor purity was estimated by ABS… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Heme biosynthesis gene expression and MAPK pathway activity across GBM tran￾scriptional subtypes (TCGA). (A) MPAS, (B) ABCB1, (C) FECH, and (D) PPOX by Verhaak subtype [29]. Boxes indicate median and interquartile range; individual samples are overlaid points. The omni…
Figure 5
Figure 5. Figure 5: MAPK pathway activity is not linked to overall survival in TCGA IDH-wildtype glioblas [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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Works this paper leans on

32 extracted references · 24 canonical work pages

  1. [9]

    Heme Biosynthesis Factors and 5-ALA Induced Fluorescence: Analysis of mRNA and Protein Expression in Fluorescing and Non-fluorescing Gliomas

    Mario Mischkulnig, Thomas Roetzer-Pejrimovsky, et al. Heme Biosynthesis Factors and 5-ALA Induced Fluorescence: Analysis of mRNA and Protein Expression in Fluorescing and Non-fluorescing Gliomas. Frontiers in Medicine, 9:907442, May 2022. ISSN 2296-858X. doi: 10.3389/fmed.2022.907442. URL https://www.frontiersin.org/articles/10.3389/fmed.2022.907442/full

    work page arXiv 2022

  2. [1]

    Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma

    Roger Stupp et al. Radiotherapy plus Concomitant and Adjuvant Temozolomide for Glioblastoma. New England Journal of Medicine, 352(10):987–996, March 2005. ISSN 0028-4793, 1533-4406. doi: 10.1056/NEJMoa043330. URLhttps://www.nejm.org/doi/10.1056/NEJMoa043330

    doi:10.1056/nejmoa043330 2005

  3. [2]

    Traylor, Mark N

    Jeffrey I. Traylor, Mark N. Pernik, Alex C. Sternisha, Samuel K. McBrayer, and Kalil G. Abdullah. Molecular and Metabolic Mechanisms Underlying Selective 5-Aminolevulinic Acid-Induced Fluorescence in Gliomas.Cancers, 13(3):580, February 2021. ISSN 2072-6694. doi: 10.3390/cancers13030580. URL https://www.mdpi.com/2072-6694/13/3/580

    work page doi:10.3390/cancers13030580 2021

  4. [3]

    Walter Stummer, Uwe Pichlmeier, Thomas Meinel, Otmar Dieter Wiestler, Friedhelm Zanella, and Hans- Jürgen Reulen. Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial.The Lancet Oncology, 7(5):392–401, May 2006. ISSN 14702045. doi: 10.1016/S1470-2045(06)70665-9. URL h...

    work page doi:10.1016/s1470-2045(06)70665-9 2006

  5. [4]

    Dailey and Amy E

    Harry A. Dailey and Amy E. Medlock. A primer on heme biosynthesis.Biological Chemistry, 403 (11-12):985–1003, November 2022. ISSN 1431-6730, 1437-4315. doi: 10.1515/hsz-2022-0205. URL https://www.degruyter.com/document/doi/10.1515/hsz-2022-0205/html

    work page doi:10.1515/hsz-2022-0205 2022

  6. [5]

    Dailey, and Amy E

    Chibuike David Obi, Tawhid Bhuiyan, Harry A. Dailey, and Amy E. Medlock. Ferrochelatase: Mapping the Intersection of Iron and Porphyrin Metabolism in the Mitochondria.Frontiers in Cell and Developmental Biology, 10:894591, May 2022. ISSN 2296-634X. doi: 10.3389/fcell.2022.894591. URL https: //www.frontiersin.org/articles/10.3389/fcell.2022.894591/full

    work page arXiv 2022

  7. [6]

    MEK reduces cancer-specific PpIX accumulation through the RSK- ABCB1 and HIF-1α-FECH axes.Scientific Reports, 10(1):22124, December 2020

    Vipin Shankar Chelakkot et al. MEK reduces cancer-specific PpIX accumulation through the RSK- ABCB1 and HIF-1α-FECH axes.Scientific Reports, 10(1):22124, December 2020. ISSN 2045-2322. doi: 10.1038/s41598-020-79144-x. URLhttps://www.nature.com/articles/s41598-020-79144-x

    work page doi:10.1038/s41598-020-79144-x 2020

  8. [7]

    A Recap of Heme Metabolism towards Understanding Protoporphyrin IX Selectivity in Cancer Cells.International Journal of Molecular Sciences, 23(14):7974, July 2022

    Martin Kiening and Norbert Lange. A Recap of Heme Metabolism towards Understanding Protoporphyrin IX Selectivity in Cancer Cells.International Journal of Molecular Sciences, 23(14):7974, July 2022. ISSN 1422-0067. doi: 10.3390/ijms23147974. URLhttps://www.mdpi.com/1422-0067/23/14/7974

    work page doi:10.3390/ijms23147974 2022

Show all 32 references
  1. [8]

    Schulz, Anika M.S

    Julia A. Schulz, Anika M.S. Hartz, and Björn Bauer. ABCB1 and ABCG2 Regulation at the Blood- Brain Barrier: Potential New Targets to Improve Brain Drug Delivery.Pharmacological Reviews, 75 (5):815–853, September 2023. ISSN 00316997. doi: 10.1124/pharmrev.120.000025. URL https:...

    2023 doi

  2. [10]

    Almiron Bonnin et al

    Damian A. Almiron Bonnin et al. Characterizing the heterogeneity in 5-aminolevulinic acid–induced fluorescence in glioblastoma.Journal of Neurosurgery, 132(6):1706–1714, June 2020. ISSN 0022- 3085, 1933-0693. doi: 10.3171/2019.2.JNS183128. URL https://thejns.org/view/journals/...

    2020 doi

  3. [11]

    Hiding in the Shadows: CPOX Expression and 5-ALA Induced Fluorescence in Human Glioma Cells.Molecular Neurobiology, 54(7):5699–5708, September 2017

    Nikolay Pustogarov et al. Hiding in the Shadows: CPOX Expression and 5-ALA Induced Fluorescence in Human Glioma Cells.Molecular Neurobiology, 54(7):5699–5708, September 2017. ISSN 0893- 7648, 1559-1182. doi: 10.1007/s12035-016-0109-7. URL http://link.springer.com/10.1007/ s120...

    2017 doi

  4. [12]

    Enhancement of Cancer-Specific Protoporphyrin IX Fluorescence by Targeting Oncogenic Ras/MEK Pathway.Theranostics, 8(8):2134–2146, 2018

    Ema Yoshioka et al. Enhancement of Cancer-Specific Protoporphyrin IX Fluorescence by Targeting Oncogenic Ras/MEK Pathway.Theranostics, 8(8):2134–2146, 2018. ISSN 1838-7640. doi: 10.7150/thno. 22641. URLhttp://www.thno.org/v08p2134.htm

    2018 doi

  5. [13]

    Comprehensive genomic characterization defines human glioblastoma genes and core pathways.Nature, 455(7216):1061–1068, October 2008

    The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways.Nature, 455(7216):1061–1068, October 2008. ISSN 0028-0836, 1476-4687. doi: 10.1038/nature07385. URLhttps://www.nature.com/articles/nature07385

    2008 doi

  6. [14]

    It’s all downstream from here: RTK/Raf/MEK/ERK pathway resistance mechanisms in glioblastoma

    Rebeca Yakubov, Ramneet Kaloti, Phooja Persaud, Anna McCracken, Gelareh Zadeh, and Severa Bunda. It’s all downstream from here: RTK/Raf/MEK/ERK pathway resistance mechanisms in glioblastoma. Journal of Neuro-Oncology, 172(2):327–345, April 2025. ISSN 0167-594X, 1573-7373. doi:...

    2025 doi

  7. [15]

    Tumor Purity as an Underlying Key Factor in Glioma.Clinical Cancer Re- search, 23(20):6279–6291, October 2017

    Changgeng Zhang et al. Tumor Purity as an Underlying Key Factor in Glioma.Clinical Cancer Re- search, 23(20):6279–6291, October 2017. ISSN 1078-0432, 1557-3265. doi: 10.1158/1078-0432. CCR-16-2598. URL https://aacrjournals.org/clincancerres/article/23/20/6279/80381/ Tumor-Puri...

    2017 doi

  8. [16]

    Inferring tumour purity and stromal and immune cell admixture from expression data.Nature Communications, 4(1):2612, October 2013

    Kosuke Yoshihara et al. Inferring tumour purity and stromal and immune cell admixture from expression data.Nature Communications, 4(1):2612, October 2013. ISSN 2041-1723. doi: 10.1038/ncomms3612. URLhttps://www.nature.com/articles/ncomms3612

    2013 doi

  9. [17]

    Mustafa Nasir-Moin et al. Localization of protoporphyrin IX during glioma-resection surgery via paired stimulated Raman histology and fluorescence microscopy.Nature Biomedical Engineering, 8(6):672–688, July 2024. ISSN 2157-846X. doi: 10.1038/s41551-024-01217-3. URL https://ww...

    2024 doi

  10. [18]

    Mario Mischkulnig, Barbara Kiesel, et al. TCGA mRNA Expression Analysis of the Heme Biosynthesis Pathway in Diffusely Infiltrating Gliomas: A Comparison of Typically 5-ALA Fluorescent and Non- Fluorescent Gliomas.Cancers, 12(8):2043, July 2020. ISSN 2072-6694. doi: 10.3390/can...

    2020 doi

  11. [19]

    A transcriptional MAPK Pathway Activity Score (MPAS) is a clinically relevant biomarker in multiple cancer types.npj Precision Oncology, 2(1):7, March 2018

    Marie-Claire Wagle et al. A transcriptional MAPK Pathway Activity Score (MPAS) is a clinically relevant biomarker in multiple cancer types.npj Precision Oncology, 2(1):7, March 2018. ISSN 2397-768X. doi: 10.1038/s41698-018-0051-4. URLhttps://www.nature.com/articles/s41698-018-0051-4

    2018 doi

  12. [20]

    Barbie et al

    David A. Barbie et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1.Nature, 462(7269):108–112, November 2009. doi: 10.1038/nature08460. URL https://www. nature.com/articles/nature08460

    2009 doi

  13. [21]

    Mesirov, and Pablo Tamayo

    Arthur Liberzon, Chet Birger, Helga Thorvaldsdóttir, Mahmoud Ghandi, Jill P. Mesirov, and Pablo Tamayo. The Molecular Signatures Database (MSigDB) hallmark gene set collection.Cell Systems, 1(6):417–425,

  14. [22]

    GSV A: gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics, 14(1):7, December 2013

    Sonja Hänzelmann, Robert Castelo, and Justin Guinney. GSV A: gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics, 14(1):7, December 2013. ISSN 1471-2105. doi: 10.1186/ 1471-2105-14-7. URL https://bmcbioinformatics.biomedcentral.com/articles/10.1186/ ...

    2013

  15. [23]

    Zheng Zhao et al. Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients.Genomics, Proteomics & Bioinformatics, 19(1):1–12, February 2021. ISSN 1672-0229, 2210-3244. doi: 10.1016/j.gpb.2020.10.005. URL https://aca...

    2021 doi

  16. [24]

    Galina Zakharova et al. Reclassification of TCGA Diffuse Glioma Profiles Linked to Transcriptomic, Epi- genetic, Genomic and Clinical Data, According to the 2021 WHO CNS Tumor Classification.International Journal of Molecular Sciences, 24(1):157, December 2022. ISSN 1422-0067....

    2021 doi

  17. [25]

    Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.Cell, 164(3):550–563, January 2016

    Michele Ceccarelli et al. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.Cell, 164(3):550–563, January 2016. ISSN 00928674. doi: 10.1016/j.cell. 2015.12.028. URLhttps://linkinghub.elsevier.com/retrieve/pii/S009286741501692X. 15

    2016 doi

  18. [26]

    Goldman et al

    Mary J. Goldman et al. Visualizing and interpreting cancer genomics data via the Xena platform.Nature Biotechnology, 38(6):675–678, June 2020. ISSN 1087-0156, 1546-1696. doi: 10.1038/s41587-020-0546-8. URLhttps://www.nature.com/articles/s41587-020-0546-8

    2020 doi

  19. [27]

    ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients

    Seongho Kim. ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients. Communications for Statistical Applications and Methods, 22(6):665–674, November 2015. ISSN 2383-

    2015

  20. [28]

    Yoav Benjamini and Yosef Hochberg. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.Journal of the Royal Statistical Society Series B: Statistical Methodology, 57(1):289–300, January 1995. ISSN 1369-7412, 1467-9868. doi: 10.1111/j.251...

    1995

  21. [29]

    Roel G. W. Verhaak et al. Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1.Cancer Cell, 17(1): 98–110, January 2010. ISSN 15356108. doi: 10.1016/j.ccr.2009.12.020. URL https://link...

    2010 doi

  22. [30]

    Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment.Cancer Cell, 32(1):42–56.e6, July 2017

    Qianghu Wang et al. Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment.Cancer Cell, 32(1):42–56.e6, July 2017. ISSN 15356108. doi: 10.1016/j.ccell.2017.06.003. URL https://linkinghub.elsevier.com/retrieve/...

    2017 doi

  23. [2015]

    doi: 10.1016/j.cels.2015.12.004

    2015 doi

  24. [4757]

    URL http://www.csam.or.kr/journal/view.html? doi=10.5351/CSAM.2015.22.6.665

    doi: 10.5351/CSAM.2015.22.6.665. URL http://www.csam.or.kr/journal/view.html? doi=10.5351/CSAM.2015.22.6.665

    2015 doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.