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.

Background: Gastrointestinal cancers including malignancies of the colon, rectum, stomach, pancreas, liver, and esophagus represent a significant global health burden with high morbidity and mortality. Advances in molecular oncology reveal that these cancers arise through complex genetic, epigenetic, microenvironmental, and metastatic processes. Understanding these mechanisms is essential for oncology nurses to support precision medicine and deliver effective, patient-centered care. Methods: This literature review employed a structured thematic analysis to synthesize knowledge on cancer biology concepts relevant to gastrointestinal oncology nursing. Comprehensive database searches (PubMed, CINAHL, Scopus, Google Scholar) targeted publications from 2000 to 2024 addressing genetic mutations, epigenetics, tumor microenvironment, metastasis, and nursing education. Eligible articles were critically reviewed and thematically coded to identify major themes and subthemes with clinical and nursing practice relevance. Results: Three primary themes emerged: (1) Genetic and Epigenetic Alterations, including oncogene activation, tumor suppressor inactivation, microsatellite instability, and DNA methylation; (2) Tumor Microenvironment and Immune Evasion, encompassing stromal barriers, angiogenesis, immune suppression, and intercellular signaling; and (3) Mechanisms of Metastasis, detailing local invasion, epithelial-mesenchymal transition, circulation, colonization, dormancy, and reactivation. Each theme includes nursing roles in patient education, decision-making support, therapy monitoring, and psychosocial care. Conclusion: Integrating cancer biology knowledge into nursing practice is essential for anticipating patient needs, supporting shared decision-making, and managing advanced therapies in gastrointestinal oncology. Nurses must engage in ongoing education and interdisciplinary collaboration to navigate the evolving landscape of precision oncology and improve outcomes and quality of life for patients facing these complex cancers.

Introduction: Colorectal cancer (CRC) is a remarkable global health burden, considering its high morbidity and mortality. For both resectable and unresectable colorectal cancer (CRC), chemotherapy is the first-line option. The survival rate was significantly affected by the location of the tumor and the mutation status of the KRAS gene. This study aimed to systematically synthesize current evidence on how primary tumor location and KRAS mutations influence the survival of colorectal cancer patients receiving chemotherapy. Methods: We performed a systematic search of the electronic databases with defined inclusion and exclusion criteria. Independent reviews were performed by two reviewers of the literature that was included. Methodological quality of the studies included was evaluated using the Joanna Briggs Institute (JBI) critical appraisal methods. Information was obtained and qualitatively analyzed. The analysis was performed through a qualitative measure. P-value < 0.05 is considered statistically significant. Results: A total of 11 studies were reviewed. All the studies reviewed reported improved quality of methodology. The qualitative synthesis suggested a trend toward worse survival for patients with right-sided tumors, although some studies reported no statistically significant difference between tumor locations. In contrast, the association between KRAS gene mutations and poorer survival outcomes appeared more consistent across studies. Overall, these findings indicate that KRAS mutations and, to a lesser extent, right-sided tumor location may predict unfavorable outcomes in chemotherapy-treated colorectal cancer patients. Conclusion: KRAS mutations were linked to poorer survival, while right-sided tumor location showed a less consistent but generally unfavorable trend among chemotherapy-treated colorectal cancer patients.

Background: Colorectal cancer (CRC) is responsible for nearly 10% of cancer cases and deaths and is emerging as one of the most prominent malignant diseases worldwide. Carcinoembryonic antigen (CEA) is the most widely used serum biomarker for CRC, but its limited sensitivity and specificity highlight the need for additional diagnostic and prognostic markers. The mitochondrial enzyme aldehyde dehydrogenase 1B1 (ALDH1B1) is highly expressed in CRC and cancer stem cells (CSCs), representing a novel biomarker, especially as its overexpression triggers an autoantibody response detectable in a patient’s serum. This study aims to quantify serum concentration of CEA and ALDH1B1 autoantibodies in patients with colorectal cancer (CRC) and to investigate the relationship between them. It further seeks to evaluate their prognostic potential as circulating biomarkers for CRC. The underlying hypothesis proposes that elevated CEA and ALDH1B1 autoantibody levels are positively correlated and collectively reflect the underlying cancer stem cell (CSC) burden. This correlation is assessed through a predictive equation developed in the study, providing a novel, noninvasive indicator of tumor progression and aggressiveness. Method: Blood samples were collected from 75 newly diagnosed CRC patients (stages II–IV) and 25 healthy controls. CEA and ALDH1B1 autoantibodies were measured using ELISA techniques. Statistical analyses include analysis of variance (ANOVA), paired t-tests, Duncan’s test, regression analysis, and receiver operating characteristic (ROC) curve assessments using SPSS software. Results: The findings show significantly higher CEA and ALDH1B1 autoantibody levels in CRC patients compared to the controls, though neither marker varied significantly across tumour stages, emphasising their role as indicators of tumour biology rather than tumour burden. The regression analysis revealed a significant direct relationship between CEA and ALDH1B1 autoantibody levels (β = 0.026, p < 0.001), as CEA explains 87% of the variability in ALDH1B1 autoantibody levels. The ROC analysis indicated a good diagnostic performance for CEA (AUC = 0.88) and a fair performance for ALDH1B1 autoantibodies (AUC = 0.67). Conclusion: The strong predictive relationship between CEA and ALDH1B1 autoantibodies suggests that CEA levels may indirectly reflect CSC activity, potentially guiding personalised treatment strategies targeting both bulk tumours and CSCs.