Introduction: The immune system plays an important role in controlling cancer development. Leukemia, a malignant disorder of hematopoietic tissues, is characterized by the abnormal proliferation of white blood cells. Unlike solid tumors, leukemia involves a systemic increase in aberrant blood cells rather than localized masses. This inflammatory condition is driven by immune mechanisms involving pathogenic cytokines, among which Interleukin-33 (IL-33) plays a complex and dual role. IL-33 influences the immune system and the tumor microenvironment (TME), potentially promoting or inhibiting leukemia progression depending on the context. A deeper understanding of its mechanisms could pave the way for innovative treatments targeting the IL-33 axis. Methods: This study aimed to evaluate specific biochemical, hematological, and molecular markers in patients with leukemia. Conducted from June 1, 2024, to September 1, 2024, the study included 60 Iraqi patients with leukemia and 30 healthy controls. Serum levels of interferon regulatory factor 8 (IRF8) and transforming growth factor-beta (TGF-β) were measured using Enzyme-Linked Immunosorbent Assay (ELISA). Single-nucleotide polymorphisms (SNPs) of the IL-33 gene (rs928413) were assessed using Polymerase Chain Reaction (PCR) with a resistance mutation system. Results: Revealed a highly significant increase in IRF8 levels in leukemia patients compared to controls (P < 0.0001), suggesting a strong association with the pathological state. While overall TGF-β levels did not differ significantly between groups, analysis of genetic polymorphisms indicated a potential influence of the IL-33 rs928413 genotype on TGF-β regulation in patients. Specifically, the GG genotype was associated with the highest IRF8 and TGF-β levels in patients. Although allele distribution of IL-33 rs928413 did not show a direct association with increased risk in this sample, the findings suggest a complex interplay between IL-33 gene polymorphisms, IRF8, and TGF-β in leukemia. Conclusion: Leukemia patients exhibit elevated immune markers, particularly IRF8, which may play a significant role in disease onset and progression. Further studies are warranted to elucidate the molecular mechanisms linking IL-33 gene polymorphisms to IRF8 and TGF-β function in leukemia, and to explore their potential as therapeutic targets or prognostic markers.

Background: Oral squamous cell carcinoma (OSCC) remains one of the most prevalent malignancies worldwide, with cisplatin-based chemotherapy limited by systemic toxicity and drug resistance. The present study aimed to enhance cisplatin efficacy through nanoliposomal co-delivery with natural antioxidants curcumin (Cur) and green tea extract (epigallocatechin gallate, EGCG). Methods: Nanoliposomes were prepared via the thin-film hydration technique followed by sonication and extrusion, generating six formulations: cisplatin-loaded (L-Cis), curcumin-loaded (L-Cur), green tea extract-loaded (L-EGCG), cisplatin + curcumin (L-Cis/Cur), cisplatin + green tea extract (L-Cis/EGCG), and triple co-loaded nanoliposomes containing cisplatin, curcumin, and EGCG (L-Cis/Cur/EGCG) prepared according to a 1:6:6 molar ratio. Results: Physicochemical characterization revealed nanoscale particle size (212–279 nm), uniform distribution (PDI < 0.3), negative zeta potential (−19 to −23 mV), and high encapsulation efficiencies (69–84%). Scanning electron microscopy confirmed spherical morphology with smooth, homogeneous surfaces. The MTT assay demonstrated that co-loaded and triple-loaded liposomes exhibited significantly higher cytotoxicity than single-drug formulations (p < 0.001). While L-Cis reduced cell viability to approximately 50%, L-Cur and L-EGCG showed moderate effects (~65–70% viability). Co-formulations (L-Cis/Cur and L-Cis/EGCG) further decreased viability to 25–30%, and the triple co-loaded L-Cis/Cur/EGCG (1:6:6) formulation induced the strongest cytotoxic response, consistent with synergistic drug interaction. Live/Dead fluorescence imaging corroborated these findings, showing an elevated proportion of PI-positive apoptotic cells in the co-delivery groups, particularly in the triple-loaded syste. Conclusion: Collectively, these results demonstrate that nanoliposomal co-encapsulation of cisplatin with curcumin and green tea extract enhances cytotoxic efficacy and apoptotic activity in OSCC cells compared to single-agent systems. The optimized L-Cis/Cur/EGCG (1:6:6) formulation exhibited the most favorable physicochemical properties and biological performance, highlighting its potential as a synergistic nanocarrier platform for oral cancer therapy with improved efficacy and reduced systemic toxicity.

Background: The third most common malignancy in women is cervical carcinoma (CC). worldwide and remains a major reason of cancer-related losses. between females. Even though the availability of conventional therapies, their efficacy is often limited, which has encouraged the search for novel agents that selectively target cancer cells. Recent studies suggest that bacterial biofilms may possess anticancer activity. Objective: Establish exopolysaccharides (EPS) as promising candidates for natural, cost-effective, and biologically compatible cancer therapies. Methods: In this study, the cytotoxic influence of extracellular polymeric substances (EPS) derived from Pseudomonas aeruginosa biofilms was assessed on HeLa cervical cancer cells via the methyl thiazolyl tetrazolium (MTT) test to calculate the IC50, or half-maximal inhibitory concentration. Following EPS treatment, apoptotic markers were analyzed by real-time PCR. Results: EPS exposure led to a significant, dose-dependent inhibition of HeLa cell proliferation matched to Vero cells (p<0.05). Furthermore, cervical cancer cells treated with EPS exhibited a marked rise in the expression of the pro-apoptotic gene Bax and a concomitant reduction in the expression of the anti-apoptotic gene Bcl-2 (p<0.05). Conclusion: These outcomes indicate that EPS can induce apoptosis in HeLa cells, as reflected by the upregulation of Bax and the downregulation of Bcl-2. This highlights the potential of EPS such as a promising candidate for the development of novel anticancer therapies for cervical cancer.

Introduction: Epithelial-mesenchymal transition (EMT) was an important process in colorectal cancer progression. Activated platelets and thrombocytosis had been associated with cancer progression, but the specific mechanism in triggering EMT through the transcription factor Snail1 was not fully understood. Methods: This study used an observational analytical design with a cross-sectional approach. The subjects were colorectal cancer patients who underwent blood tests to determine platelet and activated platelet levels (P-selectin) and tissue to determine Snail1 and EMT transcription factors (E-cadherin and vimentin). Statistical analysis was performed using SPSS, Python, and Google Colab. Results: This study showed a significant role of activated platelets in triggering EMT (p = 0.005), activated platelets in triggering Snail1 (p = 0.042), and Snail1 in triggering EMT (p = 0.002). Causality assessment by artificial intelligence analysis of direct acyclic graphs and Granger causality tests showed that changes in platelet activation levels significantly preceded increased Snail1 expression, which in turn was followed by increased EMT markers. In addition, a decision tree was built to predict EMT from P-selectin and Snail1 levels with an accuracy of 62%. Conclusion: There was no significant relationship between thrombocytosis and activated platelets, and no significant role of thrombocytosis in EMT was found. Thus, the results of this study indicated a significant role of activated platelets in triggering EMT through the transcription factor Snail1 in colorectal cancer.

Introduction: T-cell large granular lymphocytic leukemia (T-LGL) is a rare disorder with a frequency of less than 5% of the lymphoproliferative disorders (LPD) . T-LGL is characterized by persistent increase in LGLs (2 to 20×109 /L) on peripheral blood in absence of a reactive cause. Material and methods: In this retrospective study for a period of 66 months (January 2019 to June 2024), all the samples received in the flow cytometry lab with a suspicion of LPD were screened. A stain-lyse-wash protocol was used and samples were stained with Two to three tubes of 8-10 color combinations. The clinical and laboratory features of the patients diagnosed as T-LGL were retrieved from computerized Hospital Information System and were further analyzed. Results: A total of 341 samples were analysed during this period which were diagnosed as B cell neoplasm 87%, T cell neoplasm 8%, NK-cell neoplasm 1% and reactive lymphoid proliferation 4%. The T LGL comprised of 10 (2.9%) cases. Mean age of presentation was 57.3 years, with a male:female ratio of 1.25:1. Approximately 60% patients had BM involvement, 50% had autoimmune disorder and 40% had splenomegaly. Patients were treated with corticosteroids, weekly methotrexate and cyclosporine, if required. 7/10(70%) patients are on follow up, are stable and in remission. Two patients died while one was lost to follow up. Conclusion: The frequency of T LGL noted in our study was 2.9% of the lymphoproliferative disorders. T LGLs had an indolent course and responds well to treatment.

Background: The persistent challenge of ovarian cancer as a major driver of cancer mortality in the female population stems largely from its tendency toward late-stage identification and frequent disease relapse. The cadherin (CDH) gene family, crucial for cell-cell adhesion, plays complex roles in cancer progression. Objective: Bioinformatics analysis of the CDH gene family in ovarian cancer. Using multiple public databases. Methodology: Transcriptome analysis of cadherin (CDH) gene family in ovarian cancer was performed using Gene Expression Profiling Interactive Analysis 2 (GEPIA2). Prognostic value of differentially expressed CDH genes was assessed using Kaplan-Meier plotter Overall Survival (OS) . Protein-level validation was performed using Human Protein Atlas (HPA) portal which provides immunohistochemistry (IHC). By using GSCALite web server, the assessment of immune cell infiltration was conducted to explore correlations between cadherin expression and tumor immune microenvironment and drug sensitivity analysis was performed to evaluate candidate CDH genes as therapeutic response predictors. Results: Our findings revealed significant differential expression of several CDH genes: CDH1 and CDH4 were downregulated while CDH2, CDH6, CDH11, and CDH23 were upregulated in ovarian cancer tissues. Survival analysis identified CDH6, CDH11, and CDH23 as adverse prognostic markers correlating with poorer overall and progression-free survival, while high CDH2 and CDH4 expression associated with improved survival. Genetic alteration analysis revealed diverse genomic changes across the CDH family, with protein expression data largely corroborating transcriptomic findings. Novel associations between CDH expression and drug sensitivity emerged as potential predictive biomarkers. CDH1 and CDH11 expression correlated with Paclitaxel and Dasatinib resistance, respectively, while CDH2 and CDH6 expression indicated sensitivity to PI3K and Src kinase inhibitors. Conclusion: This study provides comprehensive molecular characterization of CDH family roles in ovarian cancer progression, prognosis, drug response, and immune regulation, establishing specific CDH members as potential diagnostic and therapeutic targets for ovarian cancer.

Background: Prostate cancer remains the most prevalent malignancy and leading cause of cancer-related mortality among men worldwide. This study investigated the diagnostic potential of serum gamma-glutamyl transferase (GGT) as a novel biomarker for Prostate cancer detection compared to established markers. Methods: A case-control study was conducted with 80 histologically confirmed Prostate cancer patients and 80 age-matched healthy controls. Serum levels of prostate-specific antigen (PSA), malondialdehyde (MDA), paraoxonase 1 (PON1), arylesterase (ARE), and GGT were quantified using ELISA. Results: Significantly elevated levels of PSA, MDA, and PON1 were observed in prostate cancer patients compared to controls (p ≤ 0.001 for all). In contrast, ARE activity was significantly reduced in patients (p ≤ 0.001). Serum GGT levels were significantly higher in prostate cancer patients than in healthy controls, though this difference did not reach statistical significance (p = 0.104). The mean difference in GGT levels between prostate cancer patients and controls was 16.17 U/L (95% CI: −2.65 to 34.99), which was not statistically significant (p = 0.104). In contrast, PSA levels exhibited a significant mean difference of 79.67 ng/mL (95% CI: 27.87 to 131.47; p ≤ 0.001). Multivariate analysis revealed a non-significant inverse correlation between MDA and GGT in the prostate cancer group (r = −0.18, p = 0.12). Conclusions: The use of serum GGT as an independent prognostic biomarker for prostate cancer has limited clinical utility due to its poor specificity and sensitivity, despite its significantly elevated levels in patients. In contrast, oxidative stress markers (MDA, PON1, ARE) and PSA have shown stronger prognostic potential, with PSA remaining the most effective single marker. The observed trends highlight the potential of oxidative stress biomarkers as complementary tools.

Background: Inflammatory blood biomarkers (IBMs), including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), pan-immune-inflammation value (PIV), and systemic inflammation index (SII), have been proposed as prognostic and predictive markers in cancer. This study evaluated their predictive value for pathological complete response (pCR), disease-free survival (DFS), overall survival (OS), and neoadjuvant chemotherapy (NACT)-related toxicities in early and locally advanced breast cancer (BC). Methods: A retrospective analysis was conducted on 284 BC patients receiving NACT. Associations between IBMs, treatment response, survival outcomes, and chemotherapy-related toxicities were analyzed. Results: -IBMs were significantly associated with chemotherapy-related toxicities. Neutrophils, lymphocytes, monocytes, NLR, SII, SIRI, and PIV (all p < 0.001) strongly predicted febrile neutropenia, along with doublet anti-HER2 therapy (p = 0.032). Predictors of neutropenia included neutrophil, monocyte, NLR, MLR, LMR, SII, SIRI, PIV (p < 0.05), HER2-positive status, and doublet anti-HER2 therapy. -Subgroup analyses showed IBM predictive performance varied by subtype. NLR predicted DFS in HER2+ patients (AUC = 0.839, p = 0.010); neutrophil count was linked to peripheral neuropathy in HR+/HER2− patients (p = 0.042). PLR and LMR showed excellent discrimination for febrile neutropenia in TNBC (AUCs > 0.92). In TNBC, MLR, SIRI, and PIV showed moderate-to-high discrimination for OS (AUCs 0.71–0.74). Neutrophil (p = 0.0058) and lymphocyte (p = 0.0248) levels were associated with pCR in HER2+ patients. HR+ subtypes showed limited IBM predictive value. Conclusion: IBMs demonstrated strong predictive value for chemotherapy-related toxicities and showed subtype-specific relevance for survival and treatment response. These findings support the integration of molecular stratification to enhance the predictive utility of IBMs in breast cancer, highlighting their clinical potential in anticipating and managing treatment-related adverse events and guiding personalized supportive care strategies.

Chemotherapy remains a cornerstone in cancer management, but its lack of selectively between malignant and normal proliferating cells leads to widespread toxicities that significantly reduce patient quality of life and treatment adherence. Recent research has highlighted the promising role of naturally derived bioactive compounds in mitigating chemotherapy induced damage. These compounds, including polyphenols, flavonoids, terpenoids, and alkaloids, exhibit antioxidant, anti-inflammatory, anti-apoptotic, and organoprotective properties through diverse molecular pathways. Agents such as curcumin, resveratrol, quercetin, betanin, theaflavin and thymoquinone have demonstrated significant efficacy in reducing oxidative stress, modulating inflammatory cytokines, stabilizing mitochondrial function, and preserving normal tissue architecture in preclinical and early clinical studies. Importantly, many of these compounds selectively protect normal cells without reducing the cytotoxic effect of chemotherapeutic agents on tumor cells. Advances in formulation technologies, such as nanoencapsulation and combination strategies, further enhance their bioavailability and clinical applicability. This review discusses the mechanistic basis, experimental evidence, and translational potential of bioactive compounds as cytoprotective agents in chemotherapy, underscoring their future role in integrative cancer care.

Improved diagnostic models for personalized Cancer profiling are required significantly, utilizing AI methods to enhance accuracy, support early detection, and inform targeted treatment strategies. Despite significant progress in cancer prediction, current approaches often struggle with issues of generalizability across diverse patient cohorts, computational inefficiencies, and managing heterogeneous data sources. This paper delves into the fast developing topic of AI-driven tumor class categorization utilizing expression of genes data. Focusing on machine learning (ML), explainable artificial intelligence (XAI), neural network, and transfer learning techniques. The integration of innovative AI methodologies is crucial for understanding complex genetic interactions, improving model interpretability through XAI, and enabling adaptive learning through transfer learning. This will allow medical practitioners to rely on AI-driven insights and provide strong, scalable solutions for everyday life applications in medicine. The analysis recognizes existing limitations, including the absence of established methods on cross-institutional sharing of information and the difficulties in maintaining model adaptation to different tumor subtypes. This work underscores the potential of AI to revolutionize cancer subtype classification, fostering advancements that could reshape personalized oncology, improve patient outcomes, and establish a new standard for precision medicine. Unlike prior reviews, this study goes beyond summarizing methods by synthesizing cross-cutting gaps across ML, neural network (NN), XAI, and transfer learning (TL) approaches. It further proposes a conceptual framework that integrates these methodologies to guide future research in developing clinically deployable and patient-centered cancer diagnostic systems.