Independently replicated across two cohorts. Wet lab is the next step.
PRR36 and DNAH5 were identified through a governed AI screen of 19,010 genes and independently replicated in the MSKCC cohort (GSE21034). Wet lab biological validation is the planned next phase.
E-DICE-R surfaced a result for further validation.
E-DICE-R's governed AI platform screened 19,010 genes across six molecular evidence types and identified PRR36 as a prostate cancer candidate — with zero prior cancer publications at the time of discovery. No human hypothesis guided the result. It was surfaced from data alone, under full governance controls.
Screened at scale. Found through governance.
No pre-selection, no hypothesis. Every gene screened equally — governance controlled the quality gates.
Two genes. Two independent datasets. Both replicate.
Both genes tested in an entirely independent cohort — GSE21034 from MSKCC — different patients, different platform, no data overlap with the discovery set. Both held up.
PRR36
TCGA-PRAD
Original cohort
MSKCC replication
DNAH5
TCGA-PRAD
Original cohort
MSKCC replication
Confidence estimates reflect strength of computational evidence — not clinical validation probability.
A deeper layer of findings. An understudied pathway appears.
After replication, a systematic literature search revealed something broader: not just an unstudied gene — an unstudied biological pathway. And DNAH5 overexpresses in isolation from its own protein complex.
The Ciliary Gap: 8 genes. Zero prostate cancer papers.
A search across all canonical axonemal dynein and ciliary motor genes returned zero prostate cancer publications for every gene in the family. Conventional research has not looked here.
Overexpressed alone — without its normal partners
DNAH5 has 10 high-confidence STRING interaction partners (scores 0.935–0.997), all ciliary components. None of the 10 appear in the 942 passing genes from the governed screen.
If the ciliary program were being activated in prostate cancer, the whole complex would be expected to rise together. Only DNAH5 rises. Two possible interpretations:
- Moonlighting: DNAH5 may be serving a non-ciliary function in prostate tumor cells — a recognized phenomenon in cancer biology
- Pathway disruption: Ciliary signaling may be dysregulated in a way distinct from other cancers
Neither interpretation is established. Both are testable in wet lab. This is an atypical expression pattern — the biological role remains unknown until further study.
Two independent paths converge on the same gene pair
PRR36 has one STRING interaction partner: ARHGEF38, a Rho GEF involved in cytoskeletal regulation and cell motility. ARHGEF38 independently appears in the 942 passing genes from the same governed screen — through a completely separate evidence path.
Two unrelated screening paths converging on the same gene pair is unlikely by chance. This provides functional context for an otherwise unknown gene:
- ARHGEF38 is a cytoskeletal regulator — biology mechanistically linked to invasion and metastasis
- The convergence is the most actionable functional lead from the current analysis
- Pattern is consistent with dysregulated cell motility — a known driver of progression
This is a mechanistic observation. Association with clinical aggressiveness requires validation against outcomes data.
What these signals may suggest — and what they do not yet establish
Points toward cytoskeletal control and cell movement. This biology is mechanistically linked to invasion, metastasis, and disease progression. The signal is consistent with a more invasive phenotype — not yet demonstrated to track with clinical outcomes such as Gleason score or recurrence.
May reflect a cellular stress state, altered intracellular transport, or dedifferentiation — patterns that can appear in aggressive tumors, but less mechanistically direct than the PRR36/ARHGEF38 signal. Biological role remains uncharacterized.
To suggest aggression association, the following are needed:
Gleason score, metastatic vs. localized samples, biochemical recurrence, or overall/progression-free survival data. Current framing: "Consistent with more aggressive biology — not yet established." Wet lab and outcomes data are the decisive next steps.
What these signals could mean for patients — if confirmed.
These are mechanistic observations from computational data, not clinical proof. What follows is what the biology may suggest — and what validation against outcomes data is needed to confirm.
Earlier, more precise risk stratification
If PRR36 and DNAH5 expression tracks with aggressive disease, they could flag higher-risk biology earlier — moving toward treating the patient's specific tumor rather than all prostate cancer identically. This requires association with Gleason score, recurrence, or outcomes data first.
A signal toward invasive potential
PRR36 + ARHGEF38 points to cytoskeletal regulation and cell motility — biology mechanistically tied to invasion and spread. If this pathway is active, it could inform decisions around surgery, radiation, or systemic therapy. Not yet demonstrated against clinical outcomes.
New mechanisms, new intervention points
Most treatments target well-studied pathways. An unstudied mechanism — whether DNAH5 repurposed outside its ciliary complex, or PRR36 feeding into cytoskeletal signaling — opens the door to more precise therapies with fewer off-target effects. Requires functional validation first.
Consistent with more aggressive biology — not yet established
To move from "mechanistic hints" to clinical claims, validation is needed against: Gleason score, metastatic vs. localized samples, biochemical recurrence, and survival data. That is exactly what the next phase addresses.
Six evidence types. Each one governed.
Every candidate was evaluated across six independent molecular evidence streams. A candidate only advances if it passes the quality gate for its evidence type. The governance layer enforces these gates — there is no manual override, no bias toward prior literature.
How PRR36 was found autonomously.
Policy-enforced quality gates at every stage — no human steering on which genes to examine, which thresholds to apply, or which candidates to advance.
Replication complete. Wet lab is next.
Computational discovery done. Independent replication complete. Wet lab experiments will determine whether PRR36 and DNAH5 behave in living cancer cells the way the data predicts.
Quantitative PCR — expression confirmation
Quantitative PCR experiments in prostate cancer cell lines will directly measure PRR36 and DNAH5 mRNA expression levels, confirming whether overexpression observed in public RNA-seq data holds in controlled laboratory conditions.
PlanningWestern blot & IHC — protein-level confirmation
RNA expression must be paired with protein-level evidence. Western blot and immunohistochemistry (IHC) assays will test whether the overexpression detected in transcriptomic data translates to measurable protein abundance in tumor tissue.
PlanningCell knockdown & overexpression assays
If a gene is truly oncogenic, disrupting its expression should affect cancer cell behavior. Knockdown and overexpression assays will test whether PRR36 and DNAH5 influence proliferation, migration, or apoptosis in prostate cancer cell lines — the first functional evidence of biological role.
Pending — follows qPCR & proteinComputational replication ≠ clinical validation
Independent computational replication across two cohorts is a strong scientific milestone — but it is not wet lab validation, and it is not a clinical claim. PRR36 and DNAH5 are computationally discovered candidates with independent replication confirmed. The next phase is biological testing. We will update this page as each stage completes and will not advance clinical claims before that evidence exists.
Validation roadmap — careful science, open progress.
We will update this page as each stage completes.
Interdisciplinary depth. Responsible execution.
This discovery was produced by a governed AI platform — but validating it requires human expertise. Our team spans the full validation chain: from computational biology and multi-omics analysis to applied mathematics, clinical research, and full-stack system delivery.
George Soto, MBA
Founder & Principal Investigator
System architect of the governed discovery platform. Designed the authority computation, quality gates, and deterministic execution pipeline that surfaced PRR36. Lead author, SPIE 2026.
Dr. Athar Hussain, PhD
Advisor — Data Science & Multi-omics
PhD Biotechnology, NIBGE-PIEAS. Leads biological validation, multi-omics integration, and interpretation of the PRR36 signal. Active research lab with undergraduate and graduate students.
Dr. Laura Fontanez, DNP
Advisor — Clinical Research
Board-certified Family Nurse Practitioner (FNP-BC). Provides clinical relevance assessment — bridging computational findings with real-world patient outcomes and healthcare settings.
We claim the method that identifies, validates, and governs its use.
E-DICE-R is a proprietary governed multi-omics pipeline. The IP is the structured, reproducible, authority-scored process — not the gene. PRR36 is a result produced by that method. The method is what we sell. The method is what we protect.
The method and process
E-DICE-R converts multi-omics discovery into a governed, repeatable decision system for identifying and prioritizing therapeutic and diagnostic targets. The invention defines a structured pipeline that ingests heterogeneous data, applies authority-based scoring and validation, and produces ranked, auditable outputs.
- Method: defined pipeline for ingesting, processing, scoring, and ranking targets from multi-omics data
- Decision engine: authority-based filtering, evidence weighting, and cross-cohort validation
- Outputs: ranked candidates for diagnostics, biomarkers, and therapeutic targeting
- System: architecture enforcing reproducibility, auditability, and deterministic workflows
- Pharma linkage: integration into target selection, validation, and drug development workflows
What our method found
PRR36 showed 3.7× overexpression in our TCGA prostate cancer analysis, with p = 2.57 × 10−21, supporting follow-up as an exploratory, under-studied candidate. These findings are statistically strong within the analyzed public dataset, but biomarker utility, novelty, IP position, and druggability still require separate validation.
- Quantitative signal: 3.7× differential expression in the prostate cancer cohort
- Statistical strength: p = 2.57 × 10−21 under the applied test model
- Data source: public TCGA dataset, de-identified tier
- Workflow: reproducible analysis pipeline with auditable outputs
- Interpretation scope: association only — not causality or clinical utility
- Next steps: cohort replication, subtype analysis, survival correlation, and functional characterization
E-DICE-R turns multi-omics discovery into a governed decision system.
E-DICE-R converts multi-omics discovery into a governed, repeatable decision system for identifying and prioritizing therapeutic and diagnostic targets. The invention defines a structured pipeline that ingests heterogeneous data, applies authority-based scoring and validation, and produces ranked, actionable outputs for clinical and drug-development use. This establishes a foundation for method, system, and application-level patent protection.
- Defined pipeline: ingestion → scoring → ranking → output
- Evidence weighting and cross-cohort validation
- Ranked outputs for diagnostics, biomarkers, and therapeutic targeting
- Reproducibility and auditability by design
- Deterministic workflows and evidence traceability
- Architecture-level protection around the ranking engine
- Structured evidence packages for pharma portfolio review
- Target selection and validation tracking
- Application-level use of identified targets, including PRR36
E-DICE-Edge SDK exposes the molecular governance layer of E-DICE-R as a deployable integration for external pipelines. This run serves as an illustrative example — governed multi-omics analysis surfacing a high-confidence, underexplored candidate with full reproducibility and evidence traceability. Through E-DICE-Edge Partner Vault, partners apply authority-based scoring, non-compensating validation gates, and deterministic audit workflows directly to their own datasets — reducing downstream validation risk. View Partner Vault pricing →