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  1. Ooi JSY, Lim CR, Hua CX, Ng JF, New SY
    Langmuir, 2023 Oct 31;39(43):15200-15207.
    PMID: 37851548 DOI: 10.1021/acs.langmuir.3c01748
    This study investigates the effect of DNA hairpins on the stabilization of gold nanoparticles (AuNPs) against salt-induced aggregation (SIA) in label-free colorimetric biosensors. AuNPs were incubated with DNA hairpins of varying stem lengths and toehold sequences, followed by the addition of NaCl, before being subjected to ultraviolet-visible (UV-vis) measurement. Results showed that hairpins with longer stems generally provide better stabilization of AuNPs (18-bp >14-bp >10-bp). No improvement was observed for 14- and 18-bp hairpins with a toehold beyond 8A, which may be attributed to saturated adsorption of hairpins on the gold surface. For 14-bp hairpins with an 8-mer homopolymeric toehold, we observed a stabilization trend of A > C > G > T, similar to the reported trend of ssDNA. For variants containing ≥50% adenine as terminal bases, introducing cytosine or guanine as preceding bases could also result in strong stabilization. As the proportion of adenine decreases, variants with guanine or thymine provide less protection against SIA, especially for guanine-rich hairpins (≥6G) that could form G-quadruplexes. Such findings could serve as guidelines for researchers to design suitable DNA hairpins for label-free AuNP-based biosensors.
  2. Yip KT, Das PK, Suria D, Lim CR, Ng GH, Liew CC
    J Exp Clin Cancer Res, 2010 Sep 16;29(1):128.
    PMID: 20846378 DOI: 10.1186/1756-9966-29-128
    BACKGROUND: Colorectal cancer (CRC) screening is key to CRC prevention and mortality reduction, but patient compliance with CRC screening is low. We previously reported a blood-based test for CRC that utilizes a seven-gene panel of biomarkers. The test is currently utilized clinically in North America for CRC risk stratification in the average-risk North American population in order to improve screening compliance and to enhance clinical decision making.

    METHODS: In this study, conducted in Malaysia, we evaluated the seven-gene biomarker panel validated in a North American population using blood samples collected from local patients. The panel employs quantitative RT-PCR (qRT-PCR) to analyze gene expression of the seven biomarkers (ANXA3, CLEC4D, TNFAIP6, LMNB1, PRRG4, VNN1 and IL2RB) that are differentially expressed in CRC patients as compared with controls. Blood samples from 210 patients (99 CRC and 111 controls) were collected, and total blood RNA was isolated and subjected to quantitative RT-PCR and data analysis.

    RESULTS: The logistic regression analysis of seven-gene panel has an area under the curve (AUC) of 0.76 (95% confidence interval: 0.70 to 0.82), 77% specificity, 61% sensitivity and 70% accuracy, comparable to the data obtained from the North American investigation of the same biomarker panel.

    CONCLUSIONS: Our results independently confirm the results of the study conducted in North America and demonstrate the ability of the seven biomarker panel to discriminate CRC from controls in blood samples drawn from a Malaysian population.

  3. Zaatar AM, Lim CR, Bong CW, Lee MM, Ooi JJ, Suria D, et al.
    J Exp Clin Cancer Res, 2012 Sep 17;31:76.
    PMID: 22986368 DOI: 10.1186/1756-9966-31-76
    BACKGROUND: Treatment protocols for nasopharyngeal carcinoma (NPC) developed in the past decade have significantly improved patient survival. In most NPC patients, however, the disease is diagnosed at late stages, and for some patients treatment response is less than optimal. This investigation has two aims: to identify a blood-based gene-expression signature that differentiates NPC from other medical conditions and from controls and to identify a biomarker signature that correlates with NPC treatment response.

    METHODS: RNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n=28) of a pre-intervention case cohort of patients whom we followed post-treatment.

    RESULTS: A blood-based gene expression signature composed of three genes - LDLRAP1, PHF20, and LUC7L3 - is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0.90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment.

    CONCLUSION: We have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.

  4. Liong ML, Lim CR, Yang H, Chao S, Bong CW, Leong WS, et al.
    PLoS One, 2012;7(9):e45802.
    PMID: 23071848 DOI: 10.1371/journal.pone.0045802
    Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test.
  5. Omar H, Lim CR, Chao S, Lee MM, Bong CW, Ooi EJ, et al.
    J Clin Gastroenterol, 2015 Feb;49(2):150-7.
    PMID: 25569223 DOI: 10.1097/MCG.0000000000000112
    Up to 25% of chronic hepatitis B (CHB) patients eventually develop hepatocellular carcinoma (HCC), a disease with poor prognosis unless detected early. This study identifies a blood-based RNA biomarker panel for early HCC detection in CHB.
  6. Hunter E, Salter M, Powell R, Dring A, Naithani T, Chatziioannou ME, et al.
    Cancers (Basel), 2023 May 10;15(10).
    PMID: 37345033 DOI: 10.3390/cancers15102696
    BACKGROUND: Unprecedented advantages in cancer treatment with immune checkpoint inhibitors (ICIs) remain limited to only a subset of patients. Systemic analyses of the regulatory 3D genome architecture linked to individual epigenetic and immunogenetic controls associated with tumour immune evasion mechanisms and immune checkpoint pathways reveal a highly prevalent molecular profile predictive of response to PD-1/PD-L1 ICIs. A clinical blood test based on a set of eight (8) 3D genomic biomarkers has been developed and validated on the basis of an observational trial to predict response to ICI therapy.

    METHODS: The predictive eight biomarker set is derived from prospective observational clinical trials, representing 280 treatments with Pembrolizumab, Atezolizumab, Durvalumab, Nivolumab, and Avelumab in a broad range of indications: melanoma, lung, hepatocellular, renal, breast, bladder, colon, head and neck, bone, brain, lymphoma, prostate, vulvar, and cervical cancers.

    RESULTS: The 3D genomic eight biomarker panel for response to immune checkpoint therapy achieved a high accuracy of 85%, sensitivity of 93%, and specificity of 82%.

    CONCLUSIONS: This study demonstrates that a 3D genomic approach can be used to develop a predictive clinical assay for response to PD-1/PD-L1 checkpoint inhibition in cancer patients.

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