A 4-Gene Signature of CDKN1, FDXR, SESN1 and PCNA Radiation Biomarkers for Prediction of Patient Radiosensitivity

Howe, Orla; White, Lisa M.; Cullen, D. Kacy; O’Brien, Gráinne; Shields, Laura; Bryant, Jane; Noone, Emma; Bradshaw, Shirley; Finn, Marie; Dunne, Mary; Shannon, Aoife M.; Armstrong, John G.; McClean, Brendan; Meade, Aidan D.; Badie, Christophe; Lyng, Fiona M.

doi:10.3390/ijms221910607

Cited by 6 publications

(3 citation statements)

References 62 publications

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“…The ionizing radiation dose between 0.05 and 0.5 Gy for CDKN1 , FXDR , and SESN1 significantly altered the gene expression in a recent study. 45 CDKN1A is a gene that binds to and inhibits the CDK1 , CDK2, and CDK4/6 complexes to control or halt cell cycle progression at the G1 and S phases. Through P53-dependent signaling pathway, this activity is regulated through ATM and CHK2 to control the cell cycle checkpoints in response to ionizing radiation.…”

Section: Discussionmentioning

confidence: 99%

Elucidating Genes and Transcription Factors of Human Peripheral Blood Lymphocytes Involved in the Cellular Response upon Exposure to Ionizing Radiation for Biodosimetry and Triage: An In Silico Approach

Gollapalli,

Radhakrishna,

et al. 2024

Journal of Health and Allied Sciences NU

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Background Gene expression–based biodosimetry is a promising method for estimating radiation dose following exposure. A panel of highly radio-responsive genes in human peripheral blood was used in the current investigation to create and evaluate a unique gene expression–based radiological biodosimetry method. Methodology In human cellular research, we reviewed the literature on genes and proteins correlating to radiation response in vivo and in vitro. We looked at two publicly accessible independent radiation response gene expression profiles (GSE1977 and GSE1725) and identified the differentially expressed genes (DEGs). Results The obtained data exhibited 42 genes with substantial differential expression, 25 of which were upregulated and 17 of which were downregulated in ionizing radiation exposure groups compared with control groups. The gene ontology enrichment analysis revealed that the hub genes are significantly involved in the regulation of the mitotic cell cycle phase transition, regulation of the mitotic cell cycle, and mitotic cell cycle checkpoint signaling. Out of the 42 DEGs, four top genes (CDK1, CCNB1, UBC, and UBB) were obtained through network centrality features. However, the multicomponent filtering procedure for radiation response genes resulted in cyclin-dependent kinase 1 (CDK1) as a critical gene in the dataset curated. Conclusion Our findings suggest the possibility of discovering novel gene connections involved in the cellular response of human peripheral blood lymphocytes upon exposure to ionizing radiation.

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Section: Discussionmentioning

confidence: 99%

Elucidating Genes and Transcription Factors of Human Peripheral Blood Lymphocytes Involved in the Cellular Response upon Exposure to Ionizing Radiation for Biodosimetry and Triage: An In Silico Approach

Gollapalli,

Radhakrishna,

et al. 2024

Journal of Health and Allied Sciences NU

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“…Subsequently, during surveillance of low-grade cancers, where cancer-specific markers offer limited actionable data, serial assessment of high-grade markers could provide a reliable trigger for biopsy, reducing the need for routine biopsies that remain an undesirable component of active surveillance. Finally, leveraging the association of specific molecular pathways (e.g., DNA damage response) with response to common treatments, including radiotherapy and androgen deprivation therapy, 52,53 molecular data from a pre-diagnostic MPS2 test could feasibly inform downstream treatment decisions following detection of aggressive cancer. This level of tumor-specific clinical guidance has not previously been plausible with non-invasive testing.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of MyProstateScore 2.0 to Detect Clinically Significant Prostate Cancer

Tosoian

Zhang

Xiao

et al. 2023

Preprint

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Background: The benefits of prostate cancer screening with serum prostate-specific antigen (PSA) have been largely offset by the high rate of negative prostate biopsies and overdiagnosis of indolent cancers. These outcomes result from the limited diagnostic accuracy of PSA for clinically significant prostate cancer and are considered a major obstacle to realizing the benefits of population-wide screening for prostate cancer. Methods: We analyzed RNAseq data from a prostate cancer compendium to identify novel transcripts associated with cancer and high-grade cancer. Predefined nomination criteria were applied to 58,724 gene targets, yielding 54 differentially expressed transcripts. We designed a custom multiplex qPCR panel for non-invasive detection of candidate transcripts in urine. The panel was applied to a development cohort of men with elevated PSA (3 to 10 ng per milliliter) that underwent prospective, standardized urine collection and prostate biopsy at the University of Michigan. Elastic net modeling was used to derive the optimal model for clinically significant (grade group 2 or higher) prostate cancer, the 18-transcript MyProstateScore 2.0 (MPS2) test. The calibrated, locked MPS2 model was assessed in a blinded, external National Cancer Institute (NCI) Early Detection Research Network (EDRN) validation cohort and compared to serum PSA, the Prostate Cancer Prevention Trial risk calculator (PCPTrc), and the MyProstateScore (MPS) test. The original MPS assay measures urinary expression of two cancer-associated markers (PCA3, TMPRSS2:ERG) and is endorsed by National Comprehensive Cancer Network guidelines for consideration prior to biopsy in the study population. Results: We performed multiplex urinary testing of 1,623 clinical specimens in total, representing the largest such cohort to our knowledge. The prospective NCI-EDRN validation population included 743 men undergoing per-protocol urine collection and prostate biopsy. The median age was 62 years, median PSA was 5.6 ng per milliliter, and 151 men (20%) had clinically significant prostate cancer on biopsy. The area under the receiver-operating characteristic curve (AUC) for clinically significant prostate cancer was 0.597 (95% Confidence Interval [CI], 0.547 to 0.646) for PSA, 0.659 (95% CI, 0.611 to 0.707) for the PCPTrc, and 0.737 (95% CI, 0.694 to 0.780) for MPS, as compared to 0.818 (95% CI, 0.781 to 0.855) for the optimal MPS2 model (MPS2+). Under a clinically applicable testing approach providing 95% sensitivity for clinically significant cancer, the specificity (equivalent to the percentage of unnecessary biopsies avoided after pre-biopsy testing) was 11% for PSA, 20% for the PCPTrc, and 23% for MPS, as compared to 41% for MPS2+. In all sub-populations, MPS2 testing provided negative predictive value (NPV) of 95% to 99% for clinically significant cancer. Conclusions: In a large, external validation population referred for prostate biopsy, the novel MPS2 assay provided exceptional sensitivity and NPV to rule out clinically significant prostate cancer. These data support the use of MPS2 as a highly accurate secondary test to reduce the harms associated with PSA screening and preserve its long-term benefits.

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“…Environmental factors such as chemicals can interact with genes and cause the gene's production rate to change. Sometimes genes are up-regulated by chemicals and production is increased (or vice-versa) (Howe et al, 2021). These expression changes are the biomarkers used in this study to find identifiable signatures of sets of genes (Ghandhi et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

R400: A novel gene signature for dose prediction in radiation exposure studies in humans

Peter

Vadrev²,

Soufan³

2022

Front. Syst. Biol.

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Radiation’s harmful effects on biological organisms have long been studied through mainly evaluating pathological changes in cells, tissues, or organs. Recently, there have been more accessible gene expression datasets relating to radiation exposure studies. This provides an opportunity to analyze responses at the molecular level toward revealing phenotypic differences. Biomarkers in toxicogenomics have been suggested as indicators of radiation exposure and seem to react differently to various dosages of radiation. This study proposes a predictive gene signature specific to radiation exposure and can be used in automatically diagnosing the exposure dose. In searching for a reliable gene set that will correctly identify the exposure dose, consideration needs to be given to the size of the set. For this reason, we experimented with the number of genes used for training and testing. Gene set sizes of 28, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1,000 were tested to find the size that provided the best accuracy across three datasets. Models were then trained and tested using multiple datasets in various ways, including an external validation. The dissimilarities between these datasets provide an analogy to real-world conditions where data from multiple sources are likely to have variances in format, settings, time parameters, participants, processes, and machine tolerances, so a robust training dataset from many heterogeneous samples should provide better predictability. All three datasets showed positive results with the correct classification of the radiation exposure dose. The average accuracy of all three models was 88% for gene sets of both 400 and 1,000 genes. R400 provided the best results when testing the three datasets used in this study. A literature validation of top selected genes shows high relevance of perturbations to adverse effects reported during cancer radiotherapy.

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A 4-Gene Signature of CDKN1, FDXR, SESN1 and PCNA Radiation Biomarkers for Prediction of Patient Radiosensitivity

Cited by 6 publications

References 62 publications

Elucidating Genes and Transcription Factors of Human Peripheral Blood Lymphocytes Involved in the Cellular Response upon Exposure to Ionizing Radiation for Biodosimetry and Triage: An In Silico Approach

Elucidating Genes and Transcription Factors of Human Peripheral Blood Lymphocytes Involved in the Cellular Response upon Exposure to Ionizing Radiation for Biodosimetry and Triage: An In Silico Approach

Development and Validation of MyProstateScore 2.0 to Detect Clinically Significant Prostate Cancer

R400: A novel gene signature for dose prediction in radiation exposure studies in humans

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