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Record W4414465186 · doi:10.1158/2326-6074.cimm25-a009

Abstract A009: Novel roles of the nuclear autoantigen LEDGF/p75 in modulating prostate cancer related inflammatory pathways and cancer patient response to immunotherapy

2025· article· en· W4414465186 on OpenAlexaboutno aff
Pedro T. Ochoa, Evelyn S. Sanchez-Hernández, Adelaide Makamure, Janani Nagasubramanya, Sharan Bir, Kai Cheng, Zhong Chen, Issac Kremsky, Charles Wang, Carlos A. Casiano

Bibliographic record

VenueCancer Immunology Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
Fundersnot available
KeywordsDownregulation and upregulationProstate cancerCancerGene silencingGene knockdownImmunotherapyTranscription factorAutoantibody

Abstract

fetched live from OpenAlex

Abstract The lens epithelium derived growth factor p75 (LEDGF/p75), encoded by the PSIP1 gene, is a stress oncoprotein that contributes to cancer chemoresistance and tumor aggressiveness through its ability to tether oncogenic transcription factors to active chromatin, promote RNA-loop resolution at transcriptionally active sites, enhance DNA repair, and maintain genomic integrity. Its depletion in prostate cancer (PCa) cells induces genomic instability, which may result in the upregulation of inflammatory gene pathways. LEDGF/p75, also known as the DFS70 nuclear autoantigen, triggers IgG autoantibodies in subsets of healthy individuals, patients with miscellaneous inflammatory conditions, and PCa patients. These autoantibodies have been shown to be associated with lower circulating levels of inflammatory markers. Our recent studies also demonstrated that LEDGF/p75 is upregulated in docetaxel (DTX)-resistant PCa cells and contributes to chemoresistance by regulating gene pathways associated with stress survival, DNA repair, and cell cycle progression. We hypothesized that its silencing in chemoresistant PCa cells may also alter immune-related gene pathways. Knockdown of LEDGF/p75 in chemoresistant PCa cells followed by RNA-seq analysis led to the identification of 970 differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA) revealed a role for LEDGF/p75 in modulating gene pathways associated with lymphocyte and inflammatory responses, since its downregulation led to upregulation of several inflammation-related genes including IL7R, IL18, SWAP70, BMI-1, ULBPI/2, RAET1E/L, TRPM4, FADD, and several MHC class I genes. This suggested that high LEDGF/p75 expression, known to be associated with genomic integrity, may suppress inflammatory responses. Protein expression of these genes was validated by Western blotting in PCa cells with high/low LEDGF/p75 expression. High expression of most of these inflammatory genes also correlated with better overall survival of cancer patients receiving PD-L1, PD-1, or CTLA-4 immunotherapy, as revealed by KM Plotter-Immunotherapy analysis. In addition, publicly available RNA-seq data for various immune cells revealed potential roles of LEDGF/p75 in promoting CD4 and CD8 T cell activation. Further, public scRNA-seq data pointed to LEDGF/p75 expression in T cell clusters for various cancer types. We conclude that as a guardian of genome integrity, LEDGF/p75 contributes to the negative regulation of inflammatory gene pathways. Understanding LEDGF/p75’s roles in cancer immunity and cancer-related autoimmunity may provide new insights into its immunomodulatory functions and its influence on cancer patient response to immunotherapy. Citation Format: Pedro T. Ochoa, Evelyn S. Sanchez-Hernandez, Adelaide Makamure, Janani Nagasubramanya, Sharan Bir, Kai Wen Cheng, Zhong Chen, Issac Kremsky, Charles Wang, Carlos A. Casiano. Novel roles of the nuclear autoantigen LEDGF/p75 in modulating prostate cancer related inflammatory pathways and cancer patient response to immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr A009.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.871

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.345
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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