Introduction

Circadian biology is increasingly recognized as a critical regulatory layer in cancer development, progression, and treatment responsiveness^1,2. Core clock genes—including PER2, BMAL1, and downstream regulators—govern transcriptional oscillations that influence key cellular processes such as cell-cycle control, DNA repair, apoptosis, immune activation, and metabolism. Disruption of circadian rhythms has been associated with carcinogenesis, increased tumor aggressiveness, and reduced therapeutic efficacy across multiple tumor types^1,3,4.

Despite the expanding evidence supporting the clinical relevance of circadian regulation, standardized laboratory methodologies for clock-gene assessment in human tumor tissues remain limited. Most existing studies rely on heterogeneous analytical approaches, lack precise time-stamping of tissue acquisition, and rarely integrate both protein- and transcript-level evaluation. This methodological fragmentation hinders the identification of robust and reproducible biomarkers, limiting the ability of translational and clinical research to compare results across studies or to explore timing-dependent therapeutic vulnerabilities. Refinement and harmonization of laboratory techniques are therefore essential for translating chronobiological insights into routine oncological practice.

PER2, BMAL1, and AR-V7 represent biologically and clinically relevant targets for circadian-aware molecular profiling. PER2 and BMAL1 are central components of the circadian transcription–translation feedback loop and play a pivotal role in maintaining rhythmic cellular homeostasis ^1,3. In contrast, AR-V7, a constitutively active androgen receptor splice variant, is a well-established mediator of therapeutic resistance in prostate cancer and may exhibit temporally regulated expression patterns ^5,6. Simultaneous assessment of these markers offers a unique opportunity to investigate both core circadian regulation and clinically actionable resistance mechanisms within a unified analytical framework.

This article introduces a pilot protocol designed to address this unmet methodological need by combining mandatory time-stamping at biopsy acquisition, standardized FFPE tissue processing, immunohistochemistry with digital quantification, RNA extraction with RT-qPCR validation, and predefined quality-control checkpoints. The proposed workflow is intended as a scalable and reproducible blueprint that can be applied across solid tumor types and ultimately validated in multicenter clinical studies. By bridging molecular chronobiology with practical laboratory implementation, this protocol aims to facilitate systematic incorporation of circadian biology into translational oncology research and future chrono-therapeutic strategies.

Methods

Study Design and Rationale

This protocol development study was designed to establish a standardized, reproducible, and circadian-aware workflow for gene expression profiling in FFPE tumor tissue, using PER2, BMAL1, and AR-V7 as representative molecular targets. These markers were selected based on their central role within the circadian transcription–translation feedback loop and their demonstrated relevance to tumor progression, immune regulation, and therapeutic resistance across multiple solid tumor types. Accumulating evidence indicates that disruption of core circadian oscillators contributes not only to oncogenic signaling but also to inter- and intra-patient variability in treatment efficacy and immune responsiveness, underscoring the need for temporally informed molecular assessment strategies ⁷.

A core methodological principle of this protocol is the integration of mandatory time-stamping at the moment of tissue acquisition, enabling capture of the biological phase at which samples are obtained. This feature addresses a critical limitation of many existing oncologic biomarker studies, which typically disregard sampling time despite mounting evidence that circadian phase substantially influences transcriptional and immunological readouts ⁸. By anchoring molecular measurements to real clock time, the protocol facilitates more accurate interpretation of circadian-linked expression patterns and minimizes temporal confounding in translational analyses. This approach aligns with observations demonstrating that the reliability and reproducibility of immune and transcriptional biomarkers are highly dependent on sampling timing, thereby increasing confidence that observed expression variability reflects underlying biological rhythms rather than technical or procedural noise.

AR-V7 was incorporated as a clinically relevant comparator biomarker due to its well-established association with therapeutic resistance in prostate cancer and its potential to exhibit circadian-dependent expression dynamics. Its inclusion serves both as a biological validation anchor within the workflow and as a translational example illustrating how circadian-aware methodologies may refine interpretation of resistance mechanisms and improve therapeutic stratification.

This pilot protocol prioritizes feasibility, standardization, and analytical performance rather than hypothesis-driven outcome testing. It is intended to provide a robust methodological foundation for subsequent validation studies involving expanded patient cohorts, multiplex circadian gene panels, and integration with high-throughput transcriptomic technologies. By emphasizing technical rigor, reproducibility, and cross-platform compatibility, the proposed workflow is designed to support scalable adoption across pathology laboratories and translational research environments, facilitating systematic incorporation of circadian biology into precision oncology and chrono-therapeutic research frameworks.

PROTOCOL

The main steps of the proposed circadian gene expression profiling workflow are summarized in Table 1.

Protocol Part 1: Tissue Collection and Time-Stamping

1. Collect tumor tissue samples as part of routine diagnostic or clinically indicated biopsy or surgical procedures, in accordance with institutional ethical approvals and standard pathology workflows.
2. Record the exact time of tissue excision at the moment of biopsy or surgical removal using real clock time (hh:mm). Register this timestamp as mandatory metadata for each specimen.
3. Document relevant clinical context at the time of tissue acquisition, including patient fasting status, recent corticosteroid exposure, timing of systemic anticancer therapy (if applicable), and other clinically relevant temporal modifiers.
4. Minimize ischemia time between tissue excision and fixation by transferring specimens promptly to fixative. Record ischemia duration in minutes as part of pre-analytical quality-control documentation.
5. Fix tissue specimens in 10% neutral-buffered formalin for a duration of 12–24 hours to preserve both protein antigenicity and RNA integrity.
6. Process fixed tissues into FFPE blocks using standardized dehydration, clearing, and paraffin embedding protocols in accordance with institutional pathology guidelines.
7. Exclude decalcified or inadequately fixed specimens from downstream molecular analysis to prevent compromised antigenicity or RNA degradation.
8. Assess tumor cellularity on hematoxylin and eosin-stained sections and include only samples with a minimum tumor content of greater than 50%.
9. Stratify samples into predefined clinical categories based on disease state and hormonal sensitivity, including hormone-sensitive and hormone-resistant, non-metastatic and metastatic prostate cancer, to enable circadian-dependent comparative analyses.
10. Assign and maintain unique sample identifiers linking each FFPE block to its corresponding timestamp, clinical metadata, and downstream analytical outputs.
11. Carry the recorded timestamp and pre-analytical metadata forward through all subsequent protocol steps, including sectioning, immunohistochemistry, RNA extraction, and data analysis, to ensure temporal consistency and accurate interpretation of circadian-linked molecular signals.

The reagents, equipment, and computational resources required for the proposed workflow are listed in Table 2.

Protocol Part 2: Immunohistochemistry and Digital Quantification

1. Prepare FFPE tissue sections by cutting serial sections at a thickness of 3–4 μm from each tumor block. Mount sections on charged glass slides and dry according to standard histopathological procedures.
2. Perform immunohistochemistry using analytically validated primary antibodies targeting PER2, BMAL1, and AR-V7. Process slides on an automated staining platform (e.g., Ventana Benchmark ULTRA or Leica Bond III) using manufacturer-recommended antigen retrieval solutions and detection chemistries.
3. Document all antibody-specific parameters, including antibody clone, supplier, dilution, incubation time, antigen retrieval method, and temperature, to ensure traceability and reproducibility across staining batches.
4. Include appropriate controls in each staining run, consisting of known positive control tissues, negative controls with primary antibody omission, and internal tissue controls where applicable.
5. Evaluate immunohistochemical staining using a semi-quantitative H-score method by assessing staining intensity (graded 0–3) and the percentage of positively stained tumor nuclei. Perform scoring independently by two experienced pathologists blinded to clinical and temporal metadata.
6. Assess inter-observer agreement by calculating intraclass correlation coefficients (ICC). Resolve discrepancies exceeding 10% through joint consensus review.
7. Digitize stained slides at 40× magnification using a whole-slide scanner and export images in a compatible format for digital analysis.
8. Load whole-slide images into QuPath (open-source digital pathology software) and perform automated tissue segmentation and nuclear detection using predefined classification models.
9. Define regions of interest (ROIs) in tumor-rich areas confirmed by pathological review, excluding necrotic, hemorrhagic, or artifact-prone regions.
10. Quantify protein expression digitally by extracting nuclear positivity percentages, staining intensity distributions, and spatial expression heatmaps. Export quantitative data for downstream integration with transcript-level measurements.
11. Optionally extend circadian profiling by including additional clock-related proteins (e.g., CRY1, CRY2, REV-ERBα, RORα, CLOCK), provided that antibody performance and analytical validation criteria are met.

All digital image analysis workflows and example QuPath scripts used for this protocol will be made publicly available via a dedicated GitHub repository upon publication, enabling transparent reproduction and adaptation by other laboratories.

Protocol Part 3: RNA Extraction and RT-qPCR Analysis

1. Obtain adjacent FFPE tissue sections (10 μm thickness) from the same tumor blocks used for immunohistochemistry to ensure spatial and biological comparability.
2. Deparaffinize tissue sections using xylene, followed by graded ethanol rehydration, according to established FFPE RNA extraction protocols.
3. Perform protease digestion and thermal incubation under controlled conditions optimized to maximize RNA recovery while minimizing fragmentation associated with FFPE preservation.
4. Isolate total RNA using a commercially available FFPE-compatible RNA extraction kit, following the manufacturer’s instructions.
5. Assess RNA quantity and quality by measuring spectrophotometric absorbance ratios (A260/A280) and determining fragment integrity metrics such as the DV200 index. Exclude samples that fail predefined quality thresholds.
6. Synthesize complementary DNA (cDNA) using standardized reverse transcription reaction volumes and enzyme formulations to ensure batch-to-batch consistency.
7. Perform quantitative PCR (RT-qPCR) using gene-specific primer sets for PER2, BMAL1, and AR-V7, with ACTB, GAPDH, and RPLP0 included as reference genes for normalization.
8. Run all RT-qPCR reactions in technical triplicate, including no-template controls and positive controls to monitor assay performance and contamination.
9. Calculate relative gene-expression values using ΔCt and ΔΔCt methodologies, generating fold-change estimates across biological and clinical sample groups.
10. Store and curate expression data in a structured format suitable for downstream statistical analysis and integration with immunohistochemically-derived protein expression metrics.
11. Optionally integrate transcript-level data with high-throughput or transcriptome-scale analytical environments (e.g., RNA-sequencing pipelines or secure computational platforms) without modification of the core protocol.

Common issues encountered during FFPE RNA extraction and RT-qPCR, together with suggested troubleshooting steps, are summarized in Table 3.

Method Validation Framework and Anticipated Performance Metrics

The proposed methodological workflow is expected to enable reproducible quantification of PER2, BMAL1, and AR-V7 expression at both protein and transcript levels, allowing integrated multi-layer comparison across clinically distinct prostate cancer states. Observable differences in expression patterns are anticipated in relation to hormonal sensitivity and metastatic status, potentially revealing circadian-dependent molecular heterogeneity along the continuum of prostate cancer progression.

Concordance between manual IHC scoring and digital image analysis outputs is expected to demonstrate high analytical agreement, supporting the robustness of the combined quantification strategy. Validation metrics will include intraclass correlation coefficients (ICC) to assess inter-observer reliability for semi-quantitative scoring and coefficients of variation (CV) to evaluate technical reproducibility across immunohistochemistry staining runs and RT-qPCR batches. In addition, Bland–Altman analyses will be applied to identify potential systematic biases between manual and digital quantification methods, ensuring objectivity and consistency across diverse tumor morphologies.

Transcript-level expression measurements generated using ΔCt and ΔΔCt methodologies are expected to align directionally with protein-level expression patterns for the same molecular targets. Occasional divergence between transcript and protein signals will be interpreted as biologically informative—potentially mirroring post-transcriptional regulation, circadian phase shifts, or tumor-intrinsic plasticity—rather than as methodological instability. Application of predefined quality-control filters based on RNA integrity indices (A260/280 ratios and DV200 thresholds) is expected to reduce analytical noise and improve statistical separation between expression profiles.

Data generated during protocol implementation will be used to establish feasibility benchmarks and to define operational thresholds for sample inclusion and exclusion, acceptable quality-control ranges, and minimum tissue requirements. These parameters will support iterative protocol refinement and provide a foundation for scaling the workflow into prospective, multicenter validation studies. By establishing rigorous and transparent performance benchmarks, the protocol minimizes inter-laboratory variability—an essential prerequisite for implementation of chronobiological biomarkers in routine clinical diagnostics and multicenter oncological trials.

Ultimately, the expected outcome of this protocol is the generation of reproducible analytical signatures that capture circadian-linked molecular behaviors in solid tumors and provide experimental justification for future chrono-therapeutic hypothesis testing and timing-informed interventional studies.

Discussion

This methodological protocol proposes a structured and reproducible approach for quantifying PER2, BMAL1, and AR-V7 expression at both the protein and transcript levels, addressing a critical gap in translational chrono-oncology research. Although circadian biology has been increasingly implicated in cancer progression, immune dynamics, and therapeutic resistance, the lack of standardized laboratory methodologies and time-anchored sampling has limited the field’s capacity to generate comparable, reproducible, and clinically interpretable datasets ^9-11. The workflow presented here integrates mandatory biopsy time-stamping, harmonized pre-analytical processing, dual-layer molecular quantification, and digital pathology, providing a coherent methodological foundation for circadian-aware biomarker research.

Use of matched FFPE sections for immunohistochemistry and RT-qPCR enables systematic cross-validation of protein- and RNA-level signals and allows detection of potential divergence between transcriptional and translational outputs. Such divergence may reflect circadian phase shifts, endocrine modulation, or tumor-intrinsic plasticity rather than analytical inconsistency, highlighting the importance of multi-level measurement strategies ^12-14. Inclusion of AR-V7 as a comparator biomarker introduces a clinically relevant resistance-associated signal and supports the hypothesis that key therapeutic resistance mechanisms may exhibit temporal modulation that cannot be adequately captured through static molecular diagnostics alone ^12,13.

The integration of manual pathology scoring with automated digital image analysis further strengthens analytical robustness and reproducibility. Exploratory scalability through computational environments such as Platforma demonstrates the feasibility of extending traditional pathology workflows into high-throughput, data-integrative analytical frameworks. This hybrid approach allows for detection of spatial and temporal heterogeneity patterns that may remain obscured with conventional semi-quantitative methods ¹⁵.

Beyond methodological standardization, this protocol addresses a broader translational gap by linking temporally anchored molecular profiling to potential therapeutic decision-making. Contemporary oncological practice largely relies on static biomarkers despite mounting evidence that treatment efficacy, immune responsiveness, and resistance trajectories may vary according to circadian phase ^14-17. By enabling systematic resolution of time-linked molecular behaviors, the proposed workflow provides a practical entry point for chrono-therapeutic hypothesis generation and testing.

Although initially applied to prostate cancer, the methodological framework is inherently scalable and adaptable to other solid tumors, including lung, breast, renal, and hepatobiliary malignancies, where circadian modulation of immune and endocrine pathways is increasingly recognized ^18,19. Ultimately, this protocol is intended as a foundational methodological reference for future prospective validation studies, expanded circadian gene panels, and transcriptomic modeling efforts. Successful validation may facilitate the translation of circadian biology from conceptual relevance into standardized clinical research practice and support the development of precision-timed therapeutic strategies ²⁰. Taken together, these observations align with growing evidence linking circadian regulation to cancer biology, immune dynamics, methodological validation frameworks, and the timing of anticancer therapies ^23-26.

Future Directions and Scaling Strategy

Future research should focus on validating this methodological framework in larger, prospectively collected clinical cohorts with standardized time-stamping and harmonized laboratory execution across participating institutions. A logical next step involves implementation of multicenter pilot studies designed to assess reproducibility, analytical stability, and operational feasibility of integrating circadian-aware biomarker profiling into routine histopathological workflows. In particular, prospectively controlled sampling with predefined timing windows may provide critical insight into whether circadian phase significantly impacts clinical interpretation of gene-expression outputs and downstream therapeutic decision-making ¹¹.

Expansion beyond PER2, BMAL1, and AR-V7 to include broader circadian regulatory networks—such as CRY1/CRY2, REV-ERBα, CLOCK, RORα, and downstream immune and metabolic pathway signatures—may enable multi-parameter circadian modeling and a more profound mechanistic understanding of tumor-intrinsic rhythmicity. This systems-level approach has the potential to reveal novel, targetable dependencies linked to the cellular clock. High-dimensional transcriptomic and proteomic profiling, supported by advanced computational resources and AI-enabled modeling environments, could be leveraged to construct circadian vulnerability maps capable of identifying tumor-specific and patient-specific optimal treatment timing windows.

Integration of chrono-molecular profiling with real-time clinical decision frameworks may inform optimally timed delivery of systemic therapies, including immune checkpoint inhibitors, targeted agents, endocrine therapies, and combination regimens. Increasing evidence indicates that treatment efficacy and toxicity profiles may vary significantly with timing of administration^11,15. Accordingly, future randomized clinical trials incorporating controlled circadian timing arms are warranted to formally evaluate whether biologically informed treatment scheduling translates into improved clinical outcomes and reduced adverse events. Development of pragmatic, time-based interventional studies may accelerate the transition of chrono-oncology from conceptual promise to standardized clinical application and reshape therapeutic delivery paradigms in oncology ^21,22.

Ultimately, successful validation of this protocol may support development of a standardized laboratory and analytical pathway applicable across multiple tumor types and biological contexts. Such a framework would establish a scalable reference platform for comparative chrono-oncological research, facilitate global collaboration through harmonized methodology, and enable real-world implementation of precision-timed treatment strategies. Parallel development of practical, clinician-oriented tools will be essential to ensure scalability, usability, and widespread adoption across diverse healthcare settings.

Conclusion

This protocol establishes a standardized and reproducible methodological framework for circadian gene expression profiling in solid tumors through integrated time-stamped sampling, matched-section immunohistochemistry, digital pathology quantification, and RT-qPCR analysis. By enabling simultaneous evaluation of PER2, BMAL1, and AR-V7 at both protein and transcript levels, it addresses a critical gap in translational chrono-oncology and provides a foundation for incorporating temporal biological context into precision oncology decision-making.

The workflow is designed for scalability, interoperability across clinical pathology environments, and future expansion to multiplex circadian biomarker panels and high-dimensional transcriptomic modelling. Validation in prospective and multicenter studies may support the development of clinically actionable chrono-therapeutic strategies and contribute to refining treatment timing, improving therapeutic response, and minimizing resistance trajectories.

Ultimately, advancing circadian-aware molecular profiling may accelerate translation of chrono-oncology from conceptual potential to routine clinical practice and enable the emergence of precision-timed oncology as an integral component of modern cancer care. This workflow may serve as a blueprint for future prospective chrono-therapeutic trials evaluating whether timing-optimized sampling and treatment delivery improve outcomes in oncology.