Tengku Ahmad Damitri Al-Astani Tengku Din

Vitamin D₃, also known as cholecalciferol, is a fat-soluble secosteroid that plays a critical role in regulating calcium and phosphate homeostasis and supporting bone health. Recent studies have highlighted its broad physiological and therapeutic applications, with particular focus on its anticancer properties. Vitamin D₃, through its active metabolite calcitriol, mediates a wide range of biological effects by modulating key cellular processes, including proliferation, differentiation, apoptosis, and angiogenesis. It is produced cutaneously after ultraviolet B exposure or obtained from dietary sources and then undergoes sequential hydroxylation in the liver and kidneys to form its biologically active compound. This review delineates the molecular underpinnings of vitamin D₃ in cancer biology, emphasizing the interplay between genomic and non-genomic signaling pathways that collectively orchestrate tumor initiation and progression. Clinical evidence indicates that D₃ supplementation shows promising therapeutic benefits as an adjunct to conventional cancer treatment, particularly in patients with vitamin D deficiency, although findings remain inconsistent across cancer types and clinical settings. Overall, current evidence supports vitamin D₃ as a safe, cost-effective, and promising adjuvant strategy in oncology; however, its routine clinical application requires validation through well-designed, large-scale randomized controlled trials to establish optimal dosing strategies, identify responsive patient populations, and confirm long-term efficacy and safety.

Background: Breast cancer outcomes improve substantially with earlier detection, yet mammography performance can be limited in dense breasts and may lead to false-positive investigations. Circulating microRNAs (miRNAs) and lipidomic/metabolomic signatures have emerged as promising minimally invasive biomarkers that could complement imaging for early-stage detection. The aim of this systematic review and meta-analysis is to evaluate the diagnostic accuracy of circulating microRNAs and lipidomic/metabolomic biomarkers for early breast cancer detection and to explore between-study heterogeneity. Materials and methods: A PRISMA 2020–compliant systematic review and meta-analysis were conducted. MEDLINE (PubMed), Web of Science, Scopus, Springer, ScienceDirect, and the Cochrane Library were searched for eligible diagnostic studies assessing circulating microRNAs and lipidomic/metabolomic biomarkers measured in serum or plasma. No language restrictions were applied. Non-English reports were screened and, when potentially eligible, translated for full-text assessment and data extraction. Studies using whole blood were excluded. Study quality was assessed using QUADAS-2. Random-effects models (DerSimonian–Laird) pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), and summary ROC (SROC) area under the curve (AUC). Heterogeneity was evaluated using Q and I ² statistics, and small-study effects were assessed using Deeks’ test. Results: Thirty-two studies (2015–2025) comprising 6,935 participants (3,697 breast cancer cases; 3,238 controls) were included. Pooled sensitivity was 0.87 (95% CI [0.83, 0.90]) and pooled specificity was 0.84 (95% CI [0.79, 0.88]), with pooled DOR 46.10 (95% CI [27.80, 76.70]), PLR 5.17 (95% CI [3.99, 6.68]), NLR 0.16 (95% CI [0.13, 0.21]), and SROC AUC 0.92 (95% CI [0.87, 0.94]). Heterogeneity was substantial (I ² = 88.29% for sensitivity; I ² = 90.20% for specificity). In subgroup analyses, serum-based studies showed higher pooled specificity than plasma-based studies. A formal threshold effect assessment did not reveal a statistically significant correlation between sensitivity and false-positive rate (Spearman ρ = − 0.334, p = 0.062). Deeks’ test suggested potential small-study effects (p = 0.043). Conclusions: Circulating microRNA and lipidomic/metabolomic biomarkers demonstrate strong overall diagnostic performance for breast cancer detection; however, substantial heterogeneity and potential small-study effects limit their immediate clinical translation. Given that most included studies used retrospective case-control designs rather than prospective screening cohorts, these biomarkers are best regarded as investigational, complementary tools rather than replacements for mammography at this stage. Future large, prospective, standardized studies with harmonized pre-analytics and prespecified thresholds are needed to support the implementation of screening or triage pathways.

Breast cancer (BC) is the most common malignancy among women, and late-stage presentation remains common in Asia, highlighting the need for affordable and minimally invasive adjuncts to imaging. This study evaluated whether fasting serum 1H NMR metabolomics could distinguish Malaysian women with newly diagnosed treatment-naïve BC from healthy controls and identify perturbed metabolic pathways. After spectral quality control exclusion, 101 participants were analyzed (BC, n = 44; control, n = 57). PCA and oOPLS-DA showed group separation, and 14 metabolites were deferred significantly after false discovery rate correction. Endogenous metabolites evaluated in BC included 3-hydroxybutyrate, citrate, glutamine, malonate, phenylalanine, dimethyl sulfone, myo-inositol, and L-carnitine, and the levels of formate, tyrosine, histamine, and p-hydroxyphenylacetate were reduced. Acetylsalicylate and salicylate were classified as exogenous aspirin-related markers and were excluded from the endogenous biomarker panel. Pathway analysis indicated disruptions in the tricarboxylic acid (TCA) cycle, glyoxylate/dicarboxylate metabolism, butanoate metabolism, and amino acid pathways. Metabolite-based classifiers achieved 0.901 accuracy on an internal held-out test partition (AUC 0.942) and 0.902 ± 0.061 accuracy in repeated stratified 5-fold cross-validation. These internally validated findings define a BC-associated serum metabolomic signature in a Southeast Asian cohort and warrant independent external validation.

Breast cancer remains a global public health challenge. This study aimed to investigate the relationships between serum levels of insulin-like growth factor binding protein 3 (IGFBP-3), insulin-like growth factor 1 (IGF-1), the IGFBP-3 A-202C polymorphism and hormone receptor subtypes: estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) with respect to breast cancer risk in Palestinian women. A cross-sectional study was conducted on 112 newly diagnosed, histopathologically confirmed breast cancer patients. Data collection included structured interviews and laboratory analyses, including biochemical, genetic, and immunohistochemical assessments. The tumor tissue samples were analyzed for ER, PR, and HER-2 status. SPSS version 28 was used for all data analysis. A high prevalence of hormone receptor positivity was observed. Among the breast cancer patients, 87.5% were ER positive and 84.8% were PR positive. A total of 75.9% were HER-2 negative. The IGFBP-3 A-202C genotype is significantly associated with PR and combined ER/PR positivity ( p = 0.020). Patients with ER(+)/PR(+) status had significantly higher serum IGF-1 and IGFBP-3 levels ( p ≤ 0.001), with IGF-1 levels positively correlated with hormone receptor status ( r _s = 0.232, p ≤ 0.001) and advanced disease stages, moderately with Stage III ( r _s = 0.191, p ≤ 0.001) and weakly correlated with Stage IV ( r _s = 0.119, p = 0.029). These findings suggest that IGF-1 and IGFBP-3 may have potential as candidate biomarkers for breast cancer risk and progression. Integrating genetic, biochemical, and hormone receptor status provides novel insights into breast cancer biology and may support future research on personalized prevention and treatment strategies, particularly in resource-limited settings such as the Gaza Strip.

Type 2 diabetes mellitus (T2DM) is a major cause of morbidity in the Middle East; however, Arab populations remain underrepresented in genomic studies. We synthesized evidence on single-nucleotide polymorphisms (SNPs) associated with T2DM risk in Egyptian and Levant (Jordan, Lebanon, Palestine, and Syria) cohorts to identify shared and population-specific loci. We conducted a PRISMA-aligned systematic review and meta-analysis (PROSPERO CRD420251162068) of case–control genetic studies published until July 2025. Eligible studies enrolled ≥ 60 participants, reported odds ratios (ORs) with 95% confidence intervals (CIs), and satisfied the Hardy–Weinberg equilibrium in controls. Pooled effects used fixed- and random-effects models according to heterogeneity (Cochran’s Q, I², and τ²). Publication bias was assessed using Egger’s and Begg’s tests and funnel plot symmetries. Sensitivity analyses included leave-one-out procedures and meta-regression using study-level moderators (case/control counts). Exploratory clustering and random forest models were used to examine gene/SNP features related to effect size. In Egyptians (45 SNPs), between-study heterogeneity was negligible (Q = 35.32, df = 43, p = 0.79; I² = 0%; τ² = 0). Strong risk associations were observed for IGFB rs2854843 (OR = 13.30, p = 0.001), MTHFR rs1801133 (OR = 4.60, p = 0.001), GCKR rs780094 (OR = 3.99, p = 0.001), and TCF7L2 rs12255372 (OR = 2.06, p = 0.0001); additional signals were observed for ADIPOQ and KCNJ11. Egger’s test suggested minor small-study effects (p = 0.0184), whereas Begg’s test did not (p = 0.721). Meta-regression indicated that larger case counts and smaller control counts were associated with inflated ORs (R² ≈ 0.15). In the Levant cohort (29 SNPs), heterogeneity was high (Q = 302.32, df = 28, p < 0.0001; I² = 90.7%). Notable loci included NAT2 rs1799931 (OR = 13.72, p = 0.043) and SH2B1 rs565131715 (OR = 7.55), with lower magnitude associations for ADIPOQ and KCNQ1. No significant publication bias was detected. Egyptian findings revealed consistent large-effect SNP associations with T2DM, whereas Levant populations showed variable context-dependent effects. Shared markers (TCF7L2, ADIPOQ, and KCNJ11) and population-specific candidates (IGFB, NAT2, and SH2B1) may inform targeted screening and risk stratification. Harmonized, adequately powered multicenter studies are needed to validate these findings and accelerate precision medicine in underrepresented Arab populations. The online version contains supplementary material available at 10.1186/s13098-026-02103-5.

Esophageal carcinoma (ESCA), comprising esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC), has a poor prognosis due to late diagnosis and incomplete molecular characterization. We sought to define the ESCA epigenomic landscape by integrating methylome and transcriptome data across both subtypes. TCGA-ESCA HumanMethylation450K and RNA-seq profiles (182 tumors: 95 ESCC and 87 EAC; 64 normal tissues) were analyzed using ELMER and Bioconductor workflows. Differentially methylated probes were annotated by genomic category, probe-gene coupling was tested by Spearman correlation, and a LASSO-penalized 12-CpG classifier was trained and externally validated in an independent GEO cohort (GSE68303; n = 43). ESCA showed pervasive enhancer hypomethylation, with 72.5% of the hypomethylated probes mapping to the distal enhancers. Methylation-expression coupling identified LINC02547 and PYGL as candidate effector genes, and enriched transcription factor motifs included AP-1 and NFIL3. The classifier achieved a cross-validated AUC of 0.996, approximately 0.99, sensitivity at 95% specificity, and reproducible performance across ESCC and EAC subtypes. Advanced disease stage was the dominant prognostic factor in multivariable survival models. Enhancer hypomethylation is a hallmark of ESCA, nominating LINC02547 and PYGL as biomarker candidates with diagnostic/prognostic promise. Limitations include retrospective design and need for experimental validation.

Background: Breast cancer (BC) continues to be one of the most commonly diagnosed cancers worldwide, predominantly affecting women. Insulin-like growth factor-1 (IGF-1) is vital for cellular growth and metabolism. Dysregulation of IGF-1 has been linked to an increased risk of cancer. We hypothesized that unhealthy lifestyle factors (e.g., poor diet, low physical activity) are associated with higher serum IGF-1 levels and an increased risk of BC. This research examines the relationship between lifestyle factors, IGF-1, and BC risk in Palestinian women in the Gaza Strip. Methods: A case-control study was conducted with 112 newly diagnosed BC women and 222 healthy controls. Data were collected using the International Physical Activity Questionnaire and the Food Frequency Questionnaire. IGF-1 levels were measured. Data were analyzed using SPSS version 28. Results: BC patients had significantly lower physical activity than controls (58.9% vs. 44.6%, p = 0.014). Eight of the 14 food groups studied were linked to reduced BC risk, with reductions of 3.4% for fruit, 3.7% for meat, 5.6% for grains, 3.0% for low-fat dairy, 16.5% for nuts, 3.3% for snacks and sweets, and 5.5% for soups and sauces, while eggs increased risk by 12.5%. Drinks and beverages were positively correlated with IGF-1 levels (rp = 0.121, p = 0.027), suggesting that these factors may influence BC risk. Conclusion: Lifestyle factors, including diet and physical activity, influence IGF-1 levels and BC risk. Public health interventions promoting healthier lifestyles may help reduce BC risk.

Head and neck cancers (HNCs) are among the six most common malignancies worldwide, with incidence projected to increase substantially by 2030. Although surgery and chemoradiation remain effective standard treatments, they are often costly, less accessible, and associated with significant functional and aesthetic morbidity. Photodynamic therapy (PDT) has emerged as a promising alternative due to its safety profile, cost-effectiveness, and ability to preserve vital structures. This narrative review examined preclinical and clinical studies evaluating PDT for HNC. A structured literature search was conducted in PubMed, Scopus, and Web of Science using terms such as “photodynamic therapy,” “head and neck cancer,” “oral squamous cell carcinoma,” “minimally invasive therapy,” “photoimmunotherapy,” “photochemical internalization,” and “nanoparticle delivery.” Only English-language, peer-reviewed articles reporting on the efficacy, safety, or mechanisms of PDT were included. Eligible studies were selected based on relevance to clinical application and innovation, and findings were organized thematically to highlight therapeutic outcomes, patient-centered advantages, and translational developments supporting broader clinical integration. PDT demonstrates several key advantages over conventional treatments, including high tumor specificity, reduced toxicity, preservation of normal tissues, and suitability for outpatient care. These benefits are especially valuable in low-resource settings. PDT also supports better functional and aesthetic outcomes by minimizing scarring and reducing hospital stays. Its compatibility with other modalities, such as chemotherapy, radiotherapy, and immunotherapy, further enhances its role in multimodal treatment strategies. Technological progress, particularly in nanoparticle-based photosensitizers and targeted light delivery, has markedly improved the precision and effectiveness of PDT. PDT holds strong potential as a safe, accessible, and cost-effective treatment for HNCs. Its favorable therapeutic profile, combined with expanding technological innovations, positions PDT as an important and increasingly relevant option within future oncologic care.

Breast cancer is a heterogeneous disease that claims 670,000 lives by 2022. Omic technologies, particularly next generation sequencing (NGS) offers promising avenues for precision medicine. American Society of Clinical Oncology (ASCO) outlines genomic testing's utility, emphasizing prognostic and diagnostic potential. This review succinctly explores NGS's evolution and clinical applications of NGS in breast cancer, thereby guiding future research to enhance patient care. Comprehensive literature searches were conducted using databases such as PubMed, Google Scholar, and ResearchGate, focusing on keywords including breast cancer, HER-2 low breast cancer, circulating tumour DNA, single-cell RNA sequencing, and next-generation sequencing. Peer-reviewed, high-quality articles published in English were selected for inclusion. Previous studies have explored the evolution of NGS technology and its clinical applications in breast cancer, including genomic and transcriptomic characterization, treatment guidance, and resistance prediction. Molecular profiling of challenging entities such as early-onset breast cancer and HER-2 low tumours was summarized, with key findings highlighted. This review also discusses emerging technologies, including circulating DNA and single-cell sequencing, as promising avenues for discovery. NGS has revealed the genomic and transcriptomic diversity of breast cancer, identifying actionable alterations associated with chemotherapy response and resistance to therapies such as trastuzumab, TKIs, and CDK4/6 inhibitors. Circulating tumour DNA (ctDNA) shows potential for diagnosis, prediction, prognosis, and monitoring, despite tumour heterogeneity. Single-cell analysis enables exploration of individual cell transcriptomes, though high costs and low throughput remain barriers to widespread adoption. HER2-low tumours continue to pose significant research challenges.

Artificial intelligence (AI), particularly deep learning, is reshaping breast cancer diagnostics in the radiology and pathology fields. This review synthesizes recent advances in mammography, digital breast tomosynthesis (DBT), ultrasound, MRI, and whole-slide imaging, with an emphasis on convolutional neural networks (CNNs), Vision Transformers (ViTs), and generative adversarial networks (GANs). When embedded within established screening and diagnostic workflows, AI systems can enhance lesion detection and triage, as well as reduce interpretive variability. However, performance and generalizability depend on dataset quality, population and vendor heterogeneity, acquisition protocols, and calibrated probability outputs; diminished performance on external datasets and miscalibration remain recurrent risks that require explicit mitigation during development and deployment of these models. Beyond detection and classification, segmentation and risk prediction models increasingly integrate imaging with clinicopathological and, where available, genomic variables to enable individualized risk stratification and follow-up planning. Data generation strategies, including GAN-based augmentation, can partially address data scarcity and class imbalance but require rigorous quality control and bias monitoring. Persistent barriers to clinical adoption include uneven external validation, domain shifts across institutions, variability in reporting standards, limited interpretability, and ethical, privacy, and regulatory constraints. Overall, AI should augment, rather than replace, the role of clinicians. Priorities for responsible integration include multi-site prospective evaluations, transparent and standardized reporting, bias mitigation, robust calibration, and lifecycle monitoring to ensure sustained safety and equity.