Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis

GRP78: A new promising candidate in colorectal cancer pathogenesis and therapy

Colorectal cancer (CRC) is a significant global health challenge, marked by varying incidence and mortality rates across different regions. The pathogenesis of CRC involves multiple stages, including initiation, promotion, progression, and metastasis, influenced by genetic and epigenetic factors. The chaperone protein glucose-regulated protein 78 (GRP78), crucial in regulating the unfolded protein response (UPR) during endoplasmic reticulum (ER) stress, plays a pivotal role in CRC pathogenesis. This review discusses the expression profile of GRP78 in CRC, highlighting its potential as a prognostic biomarker and its role in modulating the cellular mechanisms of CRC, including ER response regulation, cell proliferation, migration and invasion. The complex molecular interactions of GRP78 with key signaling pathways such as protein kinase B (Akt), Wnt, protein kinase R-like ER kinase (PERK), vascular endothelial growth factor (VEGF), and Kirsten rat sarcoma virus (Kras) are explored, elucidating its contributions to tumor survival, proliferation, invasion, and chemoresistance. GRP78's involvement in autophagy, glycolysis, and immune regulation further underscores its importance in CRC progression. The review also covers the therapeutic potential of targeting GRP78 in CRC, examining various natural products like curcumin, epigallocatechin gallate (EGCG), and aloe-emodin, which modulate GRP78 expression and activity. Additionally, GRP78's role in mediating resistance to chemotherapeutic agents like 5-fluorouracil (5-FU) and oxaliplatin is discussed, emphasizing its significance in the development of resistance mechanisms in CRC. In conclusion, GRP78 emerges as a central player in CRC pathogenesis and a promising target for therapeutic interventions aimed at improving treatment outcomes and overcoming chemoresistance in colorectal cancer.

Enhanced non-invasive machine learning approach for early colorectal cancer detection: Predictive modeling and validation in a Jordanian cohort

BACKGROUND

Colorectal cancer (CRC) ranks as the third most prevalent cancer worldwide, posing significant public health challenges. Late-stage detection often results in poor treatment outcomes, elevating mortality rates. The economic and psychological burdens of CRC treatment underscore the need for early detection.

OBJECTIVE

This study aims to enhance the early detection of colorectal cancer by employing machine learning (ML) algorithms on non-invasive features. The focus is on constructing a comprehensive dataset, analyzing non-invasive features, and developing predictive models to minimize the necessity for invasive procedures such as colonoscopy. By focusing on non-invasive, easily accessible data, the study aims to develop a model that can be widely applied without the associated risks of invasive procedures.

METHODS

A retrospective dataset of 400 patients was sourced from the colorectal cancer unit of Royal Medical Services (2021-2022). The dataset included demographic data, imaging reports, laboratory results, and clinical evaluations. The study involved three experiments, training ML models (K-Nearest Neighbors (KNN), Super Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Naïve Bayes (NB)) on the collected dataset and a public dataset to validate generalizability. The first experiment used 35 features across the ML algorithms. The second experiment focused on the most informative features. The third experiment validated the models using a public dataset, with Phase I including all data and Phase II excluding missing values.

RESULTS

The Random Forest (RF) algorithm consistently outperformed other models, achieving an accuracy of 95.8 % in the first experiment, increasing to 96.5 % in the second experiment. For the public dataset, RF accuracy was 66.0 % in Phase I and 68.9 % in Phase II. Conversely, the KNN algorithm exhibited the lowest accuracy across all experiments.

CONCLUSION

This study highlights the effectiveness of ML in early CRC detection using non-invasive techniques. The RF model demonstrated superior accuracy, suggesting its potential application in clinical settings. The research contributes valuable insights into CRC detection within the local context and emphasizes the broader applicability of ML in improving cancer diagnosis and personalized treatment.

Comparison of prognostic accuracy of HCC staging systems in patients undergoing TACE

PURPOSE

To compare the prognostic power of commonly used staging systems of hepatocellular carcinoma (HCC) for predicting overall survival after transarterial chemoembolization (TACE).

MATERIALS AND METHODS

This retrospective single center study included patients with HCC who underwent TACE between 2008 and 2019 in a single tertiary care center. After initial screening of 408 consecutive patients, 317 patients with HCC treated with conventional or drug-eluting beads-TACE were included. Five HCC staging systems (Barcelona Clinic Liver Cancer, Hong Kong Liver Cancer, Japan Integrated Staging, Cancer of the Liver Italian Program and Okuda) were compared using Kaplan Meier survival analysis and a log-rank test with overall survival (OS) as the study endpoint. Uni- and multivariate analyses of system-specific variables were applied to stratify outcomes and compare the ability to predict OS of patients after TACE. Four different measures were used to assess the homogeneity (Likelihood ratio:LR), discriminatory ability (linear trend:LT and C-index) and explanatory ability (Akaike Information Criterion:AIC).

RESULTS

The OS of the total cohort was 29.8 months. In terms of prognostic stratification, the BCLC staging system had the best performance (LT: 8.209, LR: 26.639, AIC: 317, c-index: 0.818) compared to HKLC (LT: 10.919, LR: 25.802, AIC: 443, c-index: 0.835), JIS (LT: 4.611, LR: 16.880, AIC: 449, c-index: 0.548), CLIP (LT: 6.738, LR: 13.109, AIC: 501, c-index: 0.782), and Okuda (LT: 7.185, LR: 0.760. LR: 16.356, AIC: 487, c-index: 0.760).

CONCLUSION

Across five commonly utilized international staging systems, the BCLC staging system demonstrated the greatest prognostic accuracy with respect to predicting OS of patients undergoing TACE.

Encoder-Decoder Variant Analysis for Semantic Segmentation of Gastrointestinal Tract Using UW-Madison Dataset

The gastrointestinal (GI) tract, an integral part of the digestive system, absorbs nutrients from ingested food, starting from the mouth to the anus. GI tract cancer significantly impacts global health, necessitating precise treatment methods. Radiation oncologists use X-ray beams to target tumors while avoiding the stomach and intestines, making the accurate segmentation of these organs crucial. This research explores various combinations of encoders and decoders to segment the small bowel, large bowel, and stomach in MRI images, using the UW-Madison GI tract dataset consisting of 38,496 scans. Encoders tested include ResNet50, EfficientNetB1, MobileNetV2, ResNext50, and Timm_Gernet_S, paired with decoders UNet, FPN, PSPNet, PAN, and DeepLab V3+. The study identifies ResNet50 with DeepLab V3+ as the most effective combination, assessed using the Dice coefficient, Jaccard index, and model loss. The proposed model, a combination of DeepLab V3+ and ResNet 50, obtained a Dice value of 0.9082, an IoU value of 0.8796, and a model loss of 0.117. The findings demonstrate the method's potential to improve radiation therapy for GI cancer, aiding radiation oncologists in accurately targeting tumors while avoiding healthy organs. The results of this study will assist healthcare professionals involved in biomedical image analysis.

Transforming brain cancer biomarker research with patinformatics and SWOT analysis

Brain cancer heterogeneity imposes significant challenges in diagnosis, causing high mortality. The lack of timely diagnosis intensifies these challenges, underscoring the need for improved diagnostics. Recent advancements in biomarker discovery have led to biomarker detection at ultra-low concentrations via multiplexing with biosensors, offering a promising avenue for the timely detection of brain cancer. Serving as a comprehensive resource, this review highlights the crucial role of primary biomarkers in brain cancer diagnosis via integration of patinformatics and SWOT analysis, thereby facilitating timely diagnosis and informed decision making. Furthermore, we aim to outline recent advances in brain cancer prognostics and management strategies, ultimately improving patient outcomes.

A novel lightweight deep learning based approaches for the automatic diagnosis of gastrointestinal disease using image processing and knowledge distillation techniques

BACKGROUND

Gastrointestinal (GI) diseases pose significant challenges for healthcare systems, largely due to the complexities involved in their detection and treatment. Despite the advancements in deep neural networks, their high computational demands hinder their practical use in clinical environments.

OBJECTIVE

This study aims to address the computational inefficiencies of deep neural networks by proposing a lightweight model that integrates model compression techniques, ConvLSTM layers, and ConvNext Blocks, all optimized through Knowledge Distillation (KD).

METHODS

A dataset of 6000 endoscopic images of various GI diseases was utilized. Advanced image preprocessing techniques, including adaptive noise reduction and image detail enhancement, were employed to improve accuracy and interpretability. The model's performance was assessed in terms of accuracy, computational cost, and disk space usage.

RESULTS

The proposed lightweight model achieved an exceptional overall accuracy of 99.38 %. It operates efficiently with a computational cost of 0.61 GFLOPs and occupies only 3.09 MB of disk space. Additionally, Grad-CAM visualizations demonstrated enhanced model saliency and interpretability, offering insights into the decision-making process of the model post-KD.

CONCLUSION

The proposed model represents a significant advancement in the diagnosis of GI diseases. It provides a cost-effective and efficient alternative to traditional deep neural network methods, overcoming their computational limitations and contributing valuable insights for improved clinical application.

Exploring the molecular interactions between ferroptosis and the Wnt/β-catenin signaling pathway: Implications for cancer and disease therapy

Ferroptosis, a regulated form of cell death dependent on iron and marked by lipid peroxidation, is increasingly recognized for its role in a wide array of diseases, including cancers, neurodegenerative disorders, and tissue damage. This review examines the dynamic interaction between ferroptosis and the Wnt/β-catenin signaling pathway, focusing on how Wnt surface receptors, ligands, antagonists, and associated components influence the regulation of ferroptosis. Key elements such as Frizzled receptors, Wnt ligands, and antagonists like DKK1 are shown to affect ferroptosis by altering oxidative stress, lipid dynamics, and iron metabolism. A central aspect of this interaction is the role of the destruction complex, particularly GSK-3β, which regulates ferroptosis through its upstream modulation by the AKT pathway and downstream control over NRF2, GPX4, and SLC7A11. Furthermore, the involvement of β-catenin/TCF transcription factors in the regulation of ferroptosis emphasizes the significance of this pathway in promoting cell survival and resisting ferroptosis, particularly in various cancers. Multiple cancers, including colorectal, breast, ovarian, and lung cancers, are affected by disruptions in the Wnt/ferroptosis axis, where enhanced Wnt signaling helps cancer cells evade ferroptosis and develop resistance to treatments. Beyond cancer, this axis also plays a crucial role in neurodegenerative diseases and conditions like myocardial infarction. Additionally, natural compounds have shown potential in modulating the Wnt/ferroptosis pathway, offering promising therapeutic approaches for a variety of diseases. This review highlights the molecular mechanisms of the Wnt/ferroptosis axis, paving the way for innovative treatment options in cancer and other diseases.

Research progress on artificial intelligence technology-assisted diagnosis of thyroid diseases

With the rapid development of the "Internet + Medical" model, artificial intelligence technology has been widely used in the analysis of medical images. Among them, the technology of using deep learning algorithms to identify features of ultrasound and pathological images and realize intelligent diagnosis of diseases has entered the clinical verification stage. This study is based on the application research of artificial intelligence technology in medical diagnosis and reviews the early screening and diagnosis of thyroid diseases. The cure rate of thyroid disease is high in the early stage, but once it deteriorates into thyroid cancer, the risk of death and treatment costs of the patient increase. At present, the early diagnosis of the disease still depends on the examination equipment and the clinical experience of doctors, and there is a certain misdiagnosis rate. Based on the above background, it is particularly important to explore a technology that can achieve objective screening of thyroid lesions in the early stages. This paper provides a comprehensive review of recent research on the early diagnosis of thyroid diseases using artificial intelligence technology. It integrates the findings of multiple studies and that traditional machine learning algorithms are widely used as research objects. The convolutional neural network model has a high recognition accuracy for thyroid nodules and thyroid pathological cell lesions. U-Net network model can significantly improve the recognition accuracy of thyroid nodule ultrasound images when used as a segmentation algorithm. This article focuses on reviewing the intelligent recognition technology of thyroid ultrasound images and pathological sections, hoping to provide researchers with research ideas and help clinicians achieve intelligent early screening of thyroid cancer.

Ensemble fuzzy deep learning for brain tumor detection

This research presents a novel ensemble fuzzy deep learning approach for brain Magnetic Resonance Imaging (MRI) analysis, aiming to improve the segmentation of brain tissues and abnormalities. The method integrates multiple components, including diverse deep learning architectures enhanced with volumetric fuzzy pooling, a model fusion strategy, and an attention mechanism to focus on the most relevant regions of the input data. The process begins by collecting medical data using sensors to acquire MRI images. These data are then used to train several deep learning models that are specifically designed to handle various aspects of brain MRI segmentation. To enhance the model's performance, an efficient ensemble learning method is employed to combine the predictions of multiple models, ensuring that the final decision accounts for different strengths of each individual model. A key feature of the approach is the construction of a knowledge base that stores data from training images and associates it with the most suitable model for each specific sample. During the inference phase, this knowledge base is consulted to quickly identify and select the best model for processing new test images, based on the similarity between the test data and previously encountered samples. The proposed method is rigorously tested on real-world brain MRI segmentation benchmarks, demonstrating superior performance in comparison to existing techniques. Our proposed method achieves an Intersection over Union (IoU) of 95% on the complete Brain MRI Segmentation dataset, demonstrating a 10% improvement over baseline solutions.

Decoration of copper nanoparticles (Cu2O NPs) over chitosan-guar gum: Its application in the Sonogashira cross-coupling reactions and treatment of human lung adenocarcinoma

This study outlines the sustainable synthesis of hybrid biopolymer hydrogels supported with octahedral Cu2O nanoparticles (NPs), alongside their biological assessments and characterizations. A composite hydrogel made of chitosan and guar gum (CS-GG) was employed as a template for the environmentally friendly synthesis of nanoparticles. Leveraging their electron-rich functional groups, the biopolymers acted as stabilizing agents for the Cu2O NPs and as green reductants, facilitating the reduction of copper ions. The material's physicochemical properties were thoroughly examined using advanced techniques, such as X-ray diffraction (XRD), Field-Emission Scanning Electron Microscopes (FE-SEM), Eneregy Dispersive X-ray Electron Spectroscopy (EDX), Fourier Transformed Infrared Spectroscopy (FT-IR), Transmission Electron Microscopy (TEM) and ICP-OES. The resulting CS-GG/Cu2O NPs nanocomposite was investigated as a reusable heterogeneous nanocatalyst, demonstrating its efficiency in the phosphine-free, palladium-free, and ligand-free synthesis of various stilbene derivatives with high yields through the Sonogashira coupling reaction. The catalyst showed no significant reduction in activity after being reused seven times consecutively. The cytotoxic effects of the CS-GG/Cu2O NPs nanocomposite on NCI-H661 lung cancer cells and normal cells (HUVEC) were assessed over 48 h using MTT assay. The cancer cell's viability decreased after exposure to the CS-GG/Cu2O NPs, with an IC50 value of 82 μg/mL. The CS-GG/Cu2O NPs nanocomposite controls the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) system, which in turn impacts apoptosis and cell proliferation in NCI-H661 cells, according to a detailed examination of the mTOR pathway. The pathway could act a role in the cell cycle inhibition and apoptosis induced by the CS-GG/Cu2O NPs nanocomposite. The CS-GG/Cu2O NPs nanocomposite could be a useful natural anti-cancer agent for the treatment of lung cancer.

CDCA genes as prognostic and therapeutic targets in Colon adenocarcinoma

OBJECTIVES

The study investigates the role of Cell Division Cycle Associated (CDCA) genes in colorectal cancer (COAD) by analyzing their differential expression, epigenetic alterations, prognostic significance, and functional associations.

METHODOLOGY

This study employed a detailed in silico and in vitro experiments-based methodology.

RESULTS

RT-qPCR assays reveal significantly elevated mRNA levels of CDCA2, CDCA3, CDCA4, CDCA5, CDCA7, and CDCA8 genes in COAD cell lines compared to controls. Bisulfite sequencing indicates reduced promoter methylation of CDCA gene promoters in COAD cell lines, suggesting an epigenetic regulatory mechanism. Analysis of large TCGA datasets confirms increased CDCA gene expression in COAD tissues. Survival analysis using cSurvival database demonstrates negative correlations between CDCA gene expression and patient overall survival. Additionally, Lasso regression-based models of CDCA genes predict survival outcomes in COAD patients. Investigating immune modulation, CDCA gene expression inversely correlates with immune cell infiltration and immune modulators. miRNA-mRNA network analysis identifies regulatory miRNAs targeting CDCA genes, validated by RT-qPCR showing up-regulation of has-mir-10a-5p and has-mir-20a-5p in COAD cell lines and tissues. Drug sensitivity analysis suggests resistance to specific drugs in COAD patients with elevated CDCA gene expression. Furthermore, CDCA gene expression correlates with crucial functional states in COAD, including "angiogenesis, apoptosis, differentiation, hypoxia, inflammation, and metastasis." Additional in vitro experiments revealed that CDCA2 and CDCA3 knockdown in SW480 and SW629 cells significantly reduced cell proliferation and colony formation while enhancing cell migration.

CONCLUSION

Overall, the study elucidates the multifaceted role of CDCA genes in COAD progression, providing insights into potential diagnostic, prognostic, and therapeutic implications.

Precision and efficiency in skin cancer segmentation through a dual encoder deep learning model

Skin cancer is a prevalent health concern, and accurate segmentation of skin lesions is crucial for early diagnosis. Existing methods for skin lesion segmentation often face trade-offs between efficiency and feature extraction capabilities. This paper proposes Dual Skin Segmentation (DuaSkinSeg), a deep-learning model, to address this gap by utilizing dual encoders for improved performance. DuaSkinSeg leverages a pre-trained MobileNetV2 for efficient local feature extraction. Subsequently, a Vision Transformer-Convolutional Neural Network (ViT-CNN) encoder-decoder architecture extracts higher-level features focusing on long-range dependencies. This approach aims to combine the efficiency of MobileNetV2 with the feature extraction capabilities of the ViT encoder for improved segmentation performance. To evaluate DuaSkinSeg's effectiveness, we conducted experiments on three publicly available benchmark datasets: ISIC 2016, ISIC 2017, and ISIC 2018. The results demonstrate that DuaSkinSeg achieves competitive performance compared to existing methods, highlighting the potential of the dual encoder architecture for accurate skin lesion segmentation.

Impact of Small Area Level Deprivation on Colorectal Cancer Survival: Findings from the Regional Cancer Registry in Korea

BACKGROUND/OBJECTIVES

Research on the relationship between small-area-level deprivation and cancer survival, particularly for colorectal cancer (CRC), is lacking. Therefore, we investigated the relationship among small area-level deprivation, individual-level factors, and CRC survival using data from the Busan Regional Cancer Registry.

METHODS

We analyzed 34,999 patients with CRC from the Busan Regional Cancer Registry from 2003 to 2020. The primary outcome was CRC mortality. The explanatory variables at the individual level included age, gender, cancer stage, and year of diagnosis, whereas the Deprivation Index (DI) was used at the regional level. We conducted a multilevel survival analysis with frailty to assess the impact of individual- and area-level factors on survival probabilities.

RESULTS

In the multilevel survival model, each unit increase in the DI at the area level was associated with a 6.6% decrease in survival probability. When applying Model 2 and deriving regional estimates using the empirical Bayesian estimation method, the graph of the DI (x-axis) against survival probability (y-axis) showed that the slope of the regional DI for the 3-year and 5-year survival probabilities increased compared with the 1-year rate across all stages of the disease. Additionally, the slopes were steeper for the distant stage than for the local or regional stages.

CONCLUSIONS

Small-area level deprivation negatively affects CRC survival, especially in distant-stage patients and those with longer disease duration.

CircPTK2 as a Valuable Biomarker and Treatment Target in Cancer

Circular RNA (CircRNA)s, a newly discovered type of noncoding RNAs, have been found to play a role in controlling the development and aggressiveness of tumors. Abnormal control of circRNA has been observed in various types of human cancers, including bladder cancer, hepatocellular carcinoma (HCC), breast cancer, and gastric cancer (GC). CircRNAs possess binding sites for microRNAs (miRNAs) and function as miRNA sponges in posttranscriptional regulation. This mechanism has been documented to influence the course of cancer. Significantly, among these putative circRNAs, circular RNA protein tyrosine kinase 2 (circPTK2) exhibited increased expression and displayed a substantial association with adverse clinical characteristics and a negative prognosis. The production of these transcripts occurs via a back-splicing mechanism. The enclosed conformation of circRNAs shields them from destruction and enhances their potential as biomarkers. Gaining insight into the molecular mechanisms involved in these processes would aid in the development of treatment approaches and the discovery of new tumor markers. This article provides a comprehensive assessment of the latest research on the biosynthesis and features of circRNAs. It examines the role of circPTK2 in the diagnosis, treatment, and prognosis evaluation of cancer.

An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis

As the world recovered from the coronavirus, the emergence of the monkeypox virus signaled a potential new pandemic, highlighting the need for faster and more efficient diagnostic methods. This study introduces a hybrid architecture for automatic monkeypox diagnosis by leveraging a modified grey wolf optimization model for effective feature selection and weighting. Additionally, the system uses an ensemble of classifiers, incorporating confusion based voting scheme to combine salient data features. Evaluation on public data sets, at various of training samples percentages, showed that the proposed strategy achieves promising performance. Namely, the system yielded an overall accuracy of 98.91% with testing run time of 5.5 seconds, while using machine classifiers with small number of hyper-parameters. Additional experimental comparison reveals superior performance of the proposed system over literature approaches using various metrics. Statistical analysis also confirmed that the proposed AMDS outperformed other models after running 50 times. Finally, the generalizability of the proposed model is evaluated by testing its performance on external data sets for monkeypox and COVID-19. Our model achieved an overall diagnostic accuracy of 98.00% and 99.00% on external COVID and monkeypox data sets, respectively.

Machine learning and public health policy evaluation: research dynamics and prospects for challenges

Background

Public health policy evaluation is crucial for improving health outcomes, optimizing healthcare resource allocation, and ensuring fairness and transparency in decision-making. With the rise of big data, traditional evaluation methods face new challenges, requiring innovative approaches.

Methods

This article reviews the principles, scope, and limitations of traditional public health policy evaluation methods and explores the application of machine learning in evaluating public health policies. It analyzes the specific steps for applying machine learning and provides practical examples. The challenges discussed include model interpretability, data bias, the continuation of historical health inequities, and data privacy concerns, while proposing ways to better apply machine learning in the context of big data.

Results

Machine learning techniques hold promise in overcoming some limitations of traditional methods, offering more precise evaluations of public health policies. However, challenges such as lack of model interpretability, the perpetuation of health inequities, data bias, and privacy concerns remain significant.

Discussion

To address these challenges, the article suggests integrating data-driven and theory-driven approaches to improve model interpretability, developing multi-level data strategies to reduce bias and mitigate health inequities, ensuring data privacy through technical safeguards and legal frameworks, and employing validation and benchmarking strategies to enhance model robustness and reproducibility.

Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy

The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively. In this study, we used various drug combination databases to build ML models for drug combinations against brain cancer cells. Some decision tree-based models were used for this purpose. The results were further evaluated using molecular docking and molecular dynamics (MD) simulation. The possibility of the hit drug combinations for crossing the Blood-brain barrier (BBB) was also examined. Based on the obtained results, the combination of niraparib, as the PARP inhibitor, and lapatinib, as the kinase inhibitor, exhibited more considerable outcomes with a remarkable model performance (accuracy of 0.915) and prediction confidence of 0.92. The protein tweety homolog 3 and BTB/POZ domain-containing protein 2 are the main targets of niraparib and lapatinib with - 10.2 and - 8.5 scores, respectively. Due to the outcomes, this drug combination can use the CAT1 transporter on the BBB surface and effectively cross the BBB. Based on the obtained results, niraparib-lapatinib can be a promising drug combination candidate for brain cancer treatment. This combination is worth to be examined by experimental investigation in vitro and in vivo.

Nanotechnology-based biomedical devices in the cancer diagnostics and therapy

Nanotechnology has significantly transformed the field of cancer diagnostics and therapeutics by introducing advanced biomedical devices. These nanotechnology-based devices exhibit remarkable capabilities in detecting and treating various cancers, addressing the limitations of traditional approaches, such as limited specificity and sensitivity. This review aims to explore the advancements in nanotechnology-driven biomedical devices, emphasizing their role in the diagnosis and treatment of cancer. Through a comprehensive analysis, we evaluate various nanotechnology-based devices across different cancer types, detailing their diagnostic and therapeutic effectiveness. The review also discusses FDA-approved nanotechnology products, patents, and regulatory trends, highlighting the innovation and clinical impact in oncology. Nanotechnology-based devices, including nanobots, smart pills, and multifunctional nanoparticles, enable precise targeting and treatment, reducing adverse effects on healthy tissues. Devices such as DNA-based nanorobots, quantum dots, and biodegradable stents offer noninvasive diagnostic and therapeutic options, showing high efficacy in preclinical and clinical settings. FDA-approved products underscore the acceptance of these technologies. Nanotechnology-based biomedical devices offer a promising future for oncology, with the potential to revolutionize cancer care through early detection, targeted treatment, and minimal side effects. Continued research and technological improvements are essential to fully realize their potential in personalized cancer therapy.

Sensitive Detection of Biomarker in Gingival Crevicular Fluid Based on Enhanced Electrochemiluminescence by Nanochannel-Confined Co3O4 Nanocatalyst

The sensitive detection of inflammatory biomarkers in gingival crevicular fluid (GCF) is highly desirable for the evaluation of periodontal disease. Luminol-based electrochemiluminescence (ECL) immunosensors offer a promising approach for the fast and convenient detection of biomarkers. However, luminol's low ECL efficiency under neutral conditions remains a challenge. This study developed an immunosensor by engineering an immunorecognition interface on the outer surface of mesoporous silica nanochannel film (SNF) and confining a Co3O4 nanocatalyst within the SNF nanochannels to improve the luminol ECL efficiency. The SNF was grown on an indium tin oxide (ITO) electrode using the simple Stöber solution growth method. A Co3O4 nanocatalyst was successfully confined within the SNF nanochannels through in situ electrodeposition, confirmed by X-ray photoelectron spectroscopy (XPS) and electrochemical measurements. The confined Co3O4 demonstrated excellent electrocatalytic activity, effectively enhancing luminol and H2O2 oxidation and boosting the ECL signal under neutral conditions. Using interleukin-6 (IL-6) as a proof-of-concept demonstration, the epoxy functionalization of the SNF outer surface enabled the covalent immobilization of capture antibodies, forming a specific immunorecognition interface. IL-6 binding induced immunocomplex formation, which reduced the ECL signal and allowed for quantitative detection. The immunosensor showed a linear detection range for IL-6 from 1 fg mL-1 to 10 ng mL-1, with a limit of detection (LOD) of 0.64 fg mL-1. It also demonstrated good selectivity and anti-interference capabilities, enabling the successful detection of IL-6 in artificial GCF samples.

Advancing Therapeutic Targets in IBD: Emerging Goals and Precision Medicine Approaches

Inflammatory bowel diseases (IBD) including Crohn's disease (CD) and ulcerative colitis (UC) are chronic, relapsing conditions characterized by dysregulated immune responses and persistent intestinal inflammation. This review aims to examine new potential therapeutic targets in IBD starting from the STRIDE-II statements. Key targets now include clinical remission, endoscopic remission, and biomarker normalization (such as C-reactive protein and fecal calprotectin). Moreover, histologic remission, transmural remission, and in the future molecular targets are emerging as important indicators of sustained disease control. The treatment goals for inflammatory bowel disease are varied: to relieve symptoms, prevent permanent intestinal damage, promote inflammation remission, and minimize complications. Consequently, the therapeutic targets have evolved to become broader and more ambitious. Integrating these advanced therapeutic targets has the potential to redefine IBD management by promoting deeper disease control and improved patient outcomes. Further research is essential to validate these strategies and optimize their clinical implementation.

Clustering-based binary Grey Wolf Optimisation model with 6LDCNNet for prediction of heart disease using patient data

In recent years, the healthcare data system has expanded rapidly, allowing for the identification of important health trends and facilitating targeted preventative care. Heart disease remains a leading cause of death in developed countries, often leading to consequential outcomes such as dementia, which can be mitigated through early detection and treatment of cardiovascular issues. Continued research into preventing strokes and heart attacks is crucial. Utilizing the wealth of healthcare data related to cardiac ailments, a two-stage medical data classification and prediction model is proposed in this study. Initially, Binary Grey Wolf Optimization (BGWO) is used to cluster features, with the grouped information then utilized as input for the prediction model. An innovative 6-layered deep convolutional neural network (6LDCNNet) is designed for the classification of cardiac conditions. Hyper-parameter tuning for 6LDCNNet is achieved through an improved optimization method. The resulting model demonstrates promising performance on both the Cleveland dataset, achieving a convergence of 96% for assessing severity, and the echocardiography imaging dataset, with an impressive 98% convergence. This approach has the potential to aid physicians in diagnosing the severity of cardiac diseases, facilitating early interventions that can significantly reduce mortality associated with cardiovascular conditions.

Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory

Heart disease is a category of various conditions that affect the heart, which includes multiple diseases that influence its structure and operation. Such conditions may consist of coronary artery disease, which is characterized by the narrowing or clotting of the arteries that supply blood to the heart muscle, with the resulting threat of heart attacks. Heart rhythm disorders (arrhythmias), heart valve problems, congenital heart defects present at birth, and heart muscle disorders (cardiomyopathies) are other types of heart disease. The objective of this work is to introduce the Greylag Goose Optimization (GGO) algorithm, which seeks to improve the accuracy of heart disease classification. GGO algorithm's binary format is specifically intended to choose the most effective set of features that can improve classification accuracy when compared to six other binary optimization algorithms. The bGGO algorithm is the most effective optimization algorithm for selecting the optimal features to enhance classification accuracy. The classification phase utilizes many classifiers, the findings indicated that the Long Short-Term Memory (LSTM) emerged as the most effective classifier, achieving an accuracy rate of 91.79%. The hyperparameter of the LSTM model is tuned using GGO, and the outcome is compared to six alternative optimizers. The GGO with LSTM model obtained the highest performance, with an accuracy rate of 99.58%. The statistical analysis employed the Wilcoxon signed-rank test and ANOVA to assess the feature selection and classification outcomes. Furthermore, a set of visual representations of the results was provided to confirm the robustness and effectiveness of the proposed hybrid approach (GGO + LSTM).

The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning

Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp, which can either be non-cancerous or cancerous. Therefore, early detection of colorectal cancer as the second deadliest cancer after lung cancer, can be highly beneficial. Moreover, the standard treatment for locally advanced colorectal cancer, which is widely accepted around the world, is chemoradiotherapy. Then, in this study, seven artificial intelligence models including decision tree, K-nearest neighbors, Adaboost, random forest, Gradient Boosting, multi-layer perceptron, and convolutional neural network were implemented to detect patients responder and non-responder to radiochemotherapy. For finding the potential predictors (genes), three feature selection strategies were employed including mutual information, F-classif, and Chi-Square. Based on feature selection models, four different scenarios were developed and five, ten, twenty and thirty features selected for designing a more accurate classification paradigm. The results of this study confirm that random forest, Gradient Boosting, decision tree, and K-nearest neighbors provided more accurate results in terms of accuracy, by 93.8%. Moreover, Among the feature selection methods, mutual information and F-classif showed the best results, while Chi-Square produced the worst results. Therefore, the suggested artificial intelligence models can be successfully applied as a robust approach for classification of colorectal cancer response to radiochemotherapy for medical studies.

Augmented histopathology: Enhancing colon cancer detection through deep learning and ensemble techniques

Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatment quality and the survival rate. This paper presents a comprehensive approach to enhance colon cancer detection and classification. The histopathological images are gathered from the CRC-VAL-HE-7K dataset. The images undergo preprocessing to improve quality, followed by augmentation to increase dataset size and enhance model generalization. A deep learning based transformer model is designed for efficient feature extraction and enhancing classification by incorporating a convolutional neural network (CNN). A cross-transformation model captures long-range dependencies between regions, and an attention mechanism assigns weights to highlight crucial features. To boost classification accuracy, a Siamese network distinguishes colon cancer tissue classes based on probabilities. Optimization algorithms fine-tune model parameters, categorizing colon cancer tissues into different classes. The multi-class classification performance is evaluated in the experimental evaluation, which demonstrates that the proposed model provided highest accuracy rate of 98.84%. In this research article, the proposed method achieved better performance in all analyses by comparing with other existing methods. RESEARCH HIGHLIGHTS: Deep learning-based techniques are proposed. DL methods are used to enhance colon cancer detection and classification. CRC-VAL-HE-7K dataset is utilized to enhance image quality. Hybrid particle swarm optimization (PSO) and dwarf mongoose optimization (DMO) are used. The deep learning models are tuned by implementing the PSO-DMO algorithm.

Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders

Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing; however, due to human negligence, this examination method has an elevated probability of negative findings. Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process. Robust classification results were achieved through the pre-segmented imagery in most current investigations. Conversely, cellular grouping makes reliable cervical cellular segmentation difficult. Additionally, the deep learning methods used in the existing works perform poorly on a multiclass classification when the data distribution is skewed, which is common in the cervical cancer dataset. To mitigate these restrictions in cervical cancer research, this proposed work uses a combination of four different deep-learning methods in various phases of this research. The proposed work is segregated into five phases: pre-processing, data augmentation, segmentation, feature extraction, and classification. Contrast maximization is performed in the pre-processing phase, and the images are augmented using Multi-modal Generative Adversarial Networks (m-GAN) in the second phase. In the third phase, cervical cancer images are segmented using the Seg-UNet model, which is forwarded to the feature extraction phase that employs denoising autoencoders. Finally, the classification is implemented using the Dense CapsNet model and applied to the SIPaKMeD dataset to categorize between normal, abnormal, and benign classes. The proposed system achieves an accuracy of 99.65%, which is higher than the other works in the literature.

A Machine Learning-Based Model for Preoperative Assessment and Malignancy Prediction in Patients with Atypia of Undetermined Significance Thyroid Nodules

Objectives: The aim of this study was to investigate the preoperative clinical and hematologic variables, including the neutrophil-to-lymphocyte ratio (NLR), that can be used to predict malignancy in patients with atypia of undetermined significance (AUS) thyroid nodules; we further aimed to develop a machine learning-based prediction model. Methods: We enrolled 280 patients who underwent surgery for AUS nodules at the Wonju Severance Christian Hospital between 2018 and 2022. A logistic regression-based model was trained and tested using cross-validation, with the performance evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC). Results: Among the 280 patients, 116 (41.4%) were confirmed to have thyroid malignancies. Independent predictors of malignancy included age, tumor size, and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) classification, particularly in patients under 55 years of age. The addition of NLR to these predictors significantly improved the malignancy prediction accuracy in this subgroup. Conclusions: Incorporating NLR into preoperative assessments provides a cost-effective, accessible tool for refining surgical decision making in younger patients with AUS nodules.

Enhancing metastatic colorectal cancer prediction through advanced feature selection and machine learning techniques

BACKGROUND AND AIMS

Colorectal cancer (CRC) is the third most prevalent cancer globally, posing a significant challenge due to its high rate of metastasis. Approximately 20% of patients with CRC present with distant metastases at diagnosis, and over 50% develop metastases within five years. Accurate prediction of metastasis is crucial for improving survival outcomes in patients with CRC.

METHODS

This study introduces an innovative cost-sensitive fast correlation-based filter (CS-FCBF) algorithm for feature selection, integrated with machine learning techniques to predict metastatic CRC. The CS-FCBF algorithm effectively reduced the number of genomic features from 184 to 9 critical genes: CXCL9, C2CD4B, RGCC, GFI1, BEX2, CXCL3, FOXQ1, PBK, and PLAG1. The methodology combined in vitro, in vivo, and analysis of publicly available single-cell RNA-seq datasets to validate the findings.

RESULTS

The application of the CS-FCBF algorithm led to a significant improvement in prediction model performance, with an average 21.16% increase in the area under the precision-recall curve. The nine identified genes hold potential as diagnostic biomarkers and therapeutic targets for metastatic CRC.

CONCLUSIONS

This study highlights the critical role of advanced feature selection methods, combined with machine learning, in addressing the challenge of class imbalance in medical diagnosis, particularly for CRC. Early detection of metastasis is vital, and the identified genes underscore their importance in the metastatic process of CRC. The methodology applied here offers valuable insights and paves the way for future research in other cancers or diseases that face similar diagnostic challenges.

A depth analysis of recent innovations in non-invasive techniques using artificial intelligence approach for cancer prediction

The fight against cancer, a relentless global health crisis, emphasizes the urgency for efficient and automated early detection methods. To address this critical need, this review assesses recent advances in non-invasive cancer prediction techniques, comparing conventional machine learning (CML) and deep neural networks (DNNs). Focusing on these seven major cancers, we analyze 310 publications spanning the years 2018 to 2024, focusing on detection accuracy as the key metric to identify the most effective predictive models, highlighting critical gaps in current methodologies, and suggesting directions for future research. We further delved into factors like datasets, features, and modalities to gain a comprehensive understanding of each approach's performance. Separate review tables for each cancer type and approach facilitated comparisons between top performers (accuracy exceeding 99%) and low performers (65.83 to 85.8%). Our exploration of public databases and commonly used classifiers revealed that optimal combinations of features, datasets, and models can achieve up to 100% accuracy for both CML and DNN. However, significant variations in accuracy (up to 35%) were observed, particularly when optimization was lacking. Notably, colorectal cancer exhibited the lowest accuracy (DNN 69%, CML 65.83%). A five-point comparative analysis (best/worst models, performance gap, average accuracy, and research trends) revealed that while DNN research is gaining momentum, CML approaches remain competitive, even outperforming DNN in some cases. This study presents an in-depth comparative analysis of CML and DNN techniques for cancer detection. This knowledge can inform future research directions and contribute to the development of increasingly accurate and reliable cancer detection tools.

Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers

BACKGROUND

To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU).

METHODS

Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted to identify genes with significant changes in expression levels between DFU patients and healthy controls. Following this, enrichment analyses were performed to uncover biological pathways and processes associated with these differentially expressed genes. Machine learning algorithms, including feature selection and classification techniques, were then applied to the data to pinpoint key genes that play crucial roles in the pathogenesis of DFU. An independent transcriptome dataset was used to validate the key genes identified in our study. Further analysis of single-cell datasets was conducted to investigate changes in key genes at the single-cell level.

RESULTS

Through this integrated approach, SCUBE1 and RNF103-CHMP3 were identified as key genes significantly associated with DFU. SCUBE1 was found to be involved in immune regulation, playing a role in the body's response to inflammation and infection, which are common in DFU. RNF103-CHMP3 was linked to extracellular interactions, suggesting its involvement in cellular communication and tissue repair mechanisms essential for wound healing. The reliability of our analysis results was confirmed in the independent transcriptome dataset. Additionally, the expression of SCUBE1 and RNF103-CHMP3 was examined in single-cell transcriptome data, showing that these genes were significantly downregulated in the cured DFU patient group, particularly in NK cells and macrophages.

CONCLUSION

The identification of SCUBE1 and RNF103-CHMP3 as potential biomarkers for DFU marks a significant step forward in understanding the molecular basis of the disease. These genes offer new directions for both diagnosis and treatment, with the potential for developing targeted therapies that could enhance patient outcomes. This study underscores the value of integrating computational methods with biological data to uncover novel insights into complex diseases like DFU. Future research should focus on validating these findings in larger cohorts and exploring the therapeutic potential of targeting SCUBE1 and RNF103-CHMP3 in clinical settings.

Application of classification tree to construct a predictive model for screening compliance among populations at high risk for early cancer in the upper digestive tract

BACKGROUND

Upper gastrointestinal cancer, including gastric cancer and esophageal cancer, is a group of common digestive system cancers worldwide. Although the diagnostic efficiency for esophageal cancer and gastric cancer has improved in recent years, more than 80% of patients are in the moderate and advanced stages when they seek medical treatment, with 5-year survival rates of 30.30% and 35.10%, respectively. The 5-year survival rate of esophageal cancer in urban areas is significantly lower than that in rural areas.

AIM

To construct a predictive model for screening compliance among populations at high risk for early cancer in the upper digestive tract using classification trees.

METHODS

A total of 800 farmers at high risk for early upper gastrointestinal cancer recruited in Shengzhou City from February to August 2023 were selected. The compliance with follow-up and screening results were statistically analyzed. Logistic regression and classification tree model were used to analyze the influencing factors of screening compliance in farmers at high risk for early upper gastrointestinal cancer, and receiver operating characteristic curve analysis was performed to evaluate the prediction efficiency of the model.

RESULTS

Among the 800 patients, 463 (57.88%) underwent endoscopic screening. Chronic superficial gastritis (61.12%) accounted for the highest proportion, followed by colorectal polyps (15.98%). There were statistically significant differences between the compliance group and non-compliance group in age, education, family monthly income, drinking history, family history of cancer, intention of physical examination, fear of endoscopy, reluctance to screen for being asymptomatic, awareness of cancer prevention and treatment, and health literacy (P < 0.05). Logistic regression analysis showed that factors such as family monthly income, history of alcohol consumption, willingness to undergo health checkups, awareness of cancer prevention and control, health literacy, fear of endoscopy, and unwillingness to screen for being asymptomatic were factors affecting the compliance of farmers at high risk for screening for early cancer in the upper digestive tract (P < 0.05). The classification tree results showed that willingness to undergo health check-ups, fear of endoscopy, unwillingness to screen for asymptomatic conditions, awareness of cancer prevention and control, and having health literacy were factors influencing the compliance of high-risk farmers with screening for early cancer in the upper digestive tract. The area under the curve of the classification tree model was smaller than that of the logistic regression model, suggesting that the fitting effect of the classification tree model was better.

CONCLUSION

The willingness to undergo health check-ups, fear of endoscopy, unwillingness to screen for asymptomatic conditions, awareness of cancer prevention and control, and having a high level of health literacy are factors that influence the compliance of high-risk farmers with screening for early cancer in the upper digestive tract. The classification tree model constructed based on these factors has good predictive performance.

Connecting the dots: Computational network analysis for disease insight and drug repurposing

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

Explore Key Genes and Mechanisms Involved in Colon Cancer Progression Based on Bioinformatics Analysis

To explore underlying mechanisms related to the progression of colon cancer and identify hub genes associated with the prognosis of patients with colon cancer. GSE10950 and GSE62932 were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was utilized to screen out the differentially expressed genes (DEGs). Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on DEGs. Moreover, STRING and Cytoscape software were utilized for establishing the network of protein-protein interaction (PPI) and identifying hub genes. Afterward, data from The Cancer Genome Atlas (TCGA) was utilized for identifying prognosis-related hub genes by Kaplan-Meier survival analysis. Colon cancer cell line LOVO and human normal intestinal epithelial cell line NCM-460 were exploited to demonstrate the differential expression of selected hub genes through RT-qPCR and western blot. The LOVO cells were transfected to regulate expressions of prognosis-associated genes, followed by exploring the effects of those genes on prognosis by Cell Counting Kit-8 assay and colony-forming assay for cancer cell proliferation, cell scratch test and transwell migration assay for cancer cell migration and Annexin V-PE/7-AAD double staining as well as flow cytometry for cancer cell apoptosis. In this study, 266 common DEGs were obtained from the intersection of two datasets. The GO analysis suggested the common DEGs mainly participated in the one-carbon metabolic process, cell cycle G2/M phase transition, organelle fission, cell cycle phase transition regulation, and regulation of mitotic cell cycle phase transition. The KEGG analysis demonstrated the common DEGs were related to the p53 signaling pathway, nitrogen metabolism, mineral absorption, and cell cycle. 10 hub genes including CCNB1, KIF4A, TPX2, MT1F, PRC1, PLK4, CALD1, MMP9, CLCA1, and MMP1 were identified and CCNB1, CLCA1, and PLK4 were prognosis-related. Increased expression of CCNB1, CLCA1, and PLK4 restrained proliferation as well as migration of cancer cells and induced apoptosis of cancer cells. CCNB1, KIF4A, TPX2, MT1F, PRC1, PLK4, CALD1, MMP9, CLCA1, and MMP1 were identified as hub genes and CCNB1, CLCA1, and PLK4 could inhibit the progression of colon cancer through inhibiting proliferation as well as migration of the cancer cell and promoting apoptosis of cancer cell.

Personalized medicine: Clinical oncology on molecular view of treatment

Cancer, the second leading global cause of death, impacts both physically and emotionally. Conventional treatments such as surgeries, chemotherapy, and radiotherapy have adverse effects, driving the need for more precise approaches. Precision medicine enables more targeted treatments. Genetic mapping, alongside other molecular biology approaches, identifies specific genes, contributing to accurate prognoses. The review addresses, in clinical use, a molecular perspective on treatment. Biomarkers like alpha-fetoprotein, beta-human chorionic gonadotropin, 5-hydroxyindoleacetic acid, programmed death-1, and cytotoxic T lymphocyte-associated protein 4 are explored, providing valuable information. Bioinformatics, with an emphasis on artificial intelligence, revolutionizes the analysis of biological data, offering more accurate diagnoses. Techniques like liquid biopsy are emphasized for early detection. Precision medicine guides therapeutic strategies based on the molecular characteristics of the tumor, as evidenced in the molecular subtypes of breast cancer. Classifications allow personalized treatments, highlighting the role of trastuzumab and endocrine therapies. Despite the benefits, challenges persist, including high costs, tumor heterogeneity, and ethical issues. Overcoming obstacles requires collaboration, ensuring that advances in molecular biology translate into accessible benefits for all.

Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia

BACKGROUND

Acute myeloid leukemia (AML) is the most common malignant myeloid disorder in adults and the fifth most common malignancy in children, necessitating advanced technologies for outcome prediction.

METHOD

This study aims to enhance prognostic capabilities in AML by integrating multi-omics data, especially gene expression and methylation, through network-based feature selection methodologies. By employing artificial intelligence and network analysis, we are exploring different methods to build a machine learning model for predicting AML patient survival. We evaluate the effectiveness of combining omics data, identify the most informative method for network integration and compare the performance with standard feature selection methods.

RESULTS

Our findings demonstrate that integrating gene expression and methylation data significantly improves prediction accuracy compared to single omics data. Among network integration methods, our study identifies the best approach that improves informative feature selection for predicting patient outcomes in AML. Comparative analyses demonstrate the superior performance of the proposed network-based methods over standard techniques.

CONCLUSIONS

This research presents an innovative and robust methodology for building a survival prediction model tailored to AML patients. By leveraging multilayer network analysis for feature selection, our approach contributes to improving the understanding and prognostic capabilities in AML and laying the foundation for more effective personalized therapeutic interventions in the future.

TBC1D10B promotes tumor progression in colon cancer via PAK4‑mediated promotion of the PI3K/AKT/mTOR pathway

This study aimed to explore the expression, function, and mechanisms of TBC1D10B in colon cancer, as well as its potential applications in the diagnosis and treatment of the disease.The expression levels of TBC1D10B in colon cancer were assessed by analyzing the TCGA and CCLE databases. Immunohistochemistry analysis was conducted using tumor and adjacent non-tumor tissues from 68 colon cancer patients. Lentiviral infection techniques were employed to silence and overexpress TBC1D10B in colon cancer cells. The effects on cell proliferation, migration, and invasion were evaluated using CCK-8, EDU, wound healing, and Transwell invasion assays. Additionally, GSEA enrichment analysis was used to explore the association of TBC1D10B with biological pathways related to colon cancer. TBC1D10B was significantly upregulated in colon cancer and closely associated with patient prognosis. Silencing of TBC1D10B notably inhibited proliferation, migration, and invasion of colon cancer cells and promoted apoptosis. Conversely, overexpression of TBC1D10B enhanced these cellular functions. GSEA analysis revealed that TBC1D10B is enriched in the AKT/PI3K/mTOR signaling pathway and highly correlated with PAK4. The high expression of TBC1D10B in colon cancer is associated with poor prognosis. It influences cancer progression by regulating the proliferation, migration, and invasion capabilities of colon cancer cells, potentially acting through the AKT/PI3K/mTOR signaling pathway. These findings provide new targets and therapeutic strategies for the treatment of colon cancer.

Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach

Generally, a person's life span depends on their food consumption because it may cause deadly diseases like colorectal cancer (CRC). In 2020, colorectal cancer accounted for one million fatalities globally, representing 10% of all cancer casualties. 76,679 males and 78,213 females over the age of 59 from ten states in the United States participated in this analysis. During follow-up, 1378 men and 981 women were diagnosed with colon cancer. This prospective cohort study used 231 food items and their variants as input features to identify CRC patients. Before labelling any foods as colorectal cancer-causing foods, it is ethical to analyse facts like how many grams of food should be consumed daily and how many times a week. This research examines five classification algorithms on real-time datasets: K-Nearest Neighbour (KNN), Decision Tree (DT), Random Forest (RF), Logistic Regression with Classifier Chain (LRCC), and Logistic Regression with Label Powerset (LRLC). Then, the SMOTE algorithm is applied to deal with and identify imbalances in the data. Our study shows that eating more than 10 g/d of low-fat butter in bread (RR 1.99, CI 0.91-4.39) and more than twice a week (RR 1.49, CI 0.93-2.38) increases CRC risk. Concerning beef, eating in excess of 74 g of beef steak daily (RR 0.88, CI 0.50-1.55) and having it more than once a week (RR 0.88, CI 0.62-1.23) decreases the risk of CRC, respectively. While eating beef and dairy products in a daily diet should be cautious about quantity. Consuming those items in moderation on a regular basis will protect us against CRC risk. Meanwhile, a high intake of poultry (RR 0.2, CI 0.05-0.81), fish (RR 0.82, CI 0.31-2.16), and pork (RR 0.67, CI 0.17-2.65) consumption negatively correlates to CRC hazards.

A review of big data technology and its application in cancer care

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future.

Identification and immunoinfiltration analysis of key genes in ulcerative colitis using WGCNA

Objective

Ulcerative colitis (UC) is a chronic non-specific inflammatory bowel disease characterized by an unclear pathogenesis. This study aims to screen out key genes related to UC pathogenesis.

Methods

Bioinformatics analysis was conducted for screening key genes linked to UC pathogenesis, and the expression of the screened key genes was verified by establishing a UC mouse model.

Results

Through bioinformatics analysis, five key genes were obtained. Subsequent infiltration analysis revealed seven significantly different immune cell types between the UC and general samples. Additionally, animal experiment results illustrated markedly decreased body weight, visible colonic shortening and damage, along with a significant increase in the DAI score of the DSS-induced mice in the UC group in comparison with the NC group. In addition, H&E staining results demonstrated histological changes including marked inflammatory cell infiltration, loss of crypts, and epithelial destruction in the colon mucosa epithelium. qRT-PCR analysis indicated a down-regulation of ABCG2 and an up-regulation of IL1RN, REG4, SERPINB5 and TRIM29 in the UC mouse model. Notably, this observed trend showed a significant dependence on the concentration of DSS, with the mouse model of UC induced by 7% DSS demonstrating a more severe disease state compared to that induced by 5% DSS.

Conclusion

ABCG2, IL1RN, REG4, SERPINB5 and TRIM29 were screened out as key genes related to UC by bioinformatics analysis. The expression of ABCG2 was down-regulated, and that of IL1RN, REG4, SERPINB5 and TRIM29 were up-regulated in UC mice as revealed by animal experiments.

Serum Interleukins 8, 17, and 33 as Potential Biomarkers of Colon Cancer

This research investigated the serum levels of three interleukins (IL8, IL17A, and IL33) and the possible relationships between them in healthy people and colon cancer patients at different stages. This study involved 82 participants, 42 of whom had colon cancer and 40 were healthy individuals. The cancer patients were classified into four groups according to the TNM staging classification of colon and rectal cancer. Serum levels of the interleukins were measured by the ELISA test. The data were analyzed statistically to compare the demographic characteristics, the interleukin levels across cancer stages, and the correlation between interleukins in both groups. The results showed that women had more early-stage colon cancer diagnoses, while men had more advanced-stage cancer diagnoses. Stage two colon cancer was more common in older people. Younger people, men, and those with early-stage colon cancer had higher levels of interleukins. The levels of IL8 and IL17A were higher in the cancer group, while the level of IL33 was higher in the healthy group. There was a strong correlation between IL8 and IL17A levels in both groups (p = 0.001). IL17A influenced the level of IL33 in the cancer group (p = 0.007). This study suggested that cytokine variation profiles could be useful for detecting colon cancer and predicting its outcome.

Weighted gene co-expression network analysis for hub genes in colorectal cancer

BACKGROUND

This study is designed to explore hub genes participating in colorectal cancer (CRC) development through weighted gene co-expression network analysis (WGCNA).

METHODS

Expression profiles of CRC and normal samples were retrieved from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA), and were subjected to WGCNA to filter differentially expressed genes with significant association with CRC. Functional enrichment analysis and protein-protein interaction (PPI) analysis were carried out to filter the candidate genes, further and survival analysis was performed for the candidate genes to obtain potential regulatory hub genes in CRC. Expression analysis was conducted for the candidate genes and a multifactor model was established.

RESULTS

After differential analysis and WGCNA, 289 candidate genes were filtered from the GEO and TCGA. Further functional enrichment analysis demonstrated possible regulatory pathways and functions. PPI analysis filtered 15 hub genes and survival analysis indicated a significant correlation of CLCA1, CLCA4, and CPT1A with prognosis of patients with CRC. The multifactor Cox risk model established based on the three genes revealed that if the three genes were a gene set, they had well predictive capacity for the prognosis of patients with CRC.

CONCLUSIONS

CLCA1, CLCA4, and CPT1A express at low levels in CRC and function as core anti-tumor genes. As a gene set, they can predict prognosis well.

Identification of prognostic coagulation-related signatures in clear cell renal cell carcinoma through integrated multi-omics analysis and machine learning

Clear cell renal cell carcinoma is a threat to public health with high morbidity and mortality. Clinical evidence has shown that cancer-associated thrombosis poses significant challenges to treatments, including drug resistance and difficulties in surgical decision-making in ccRCC. However, the coagulation pathway, one of the core mechanisms of cancer-associated thrombosis, recently found closely related to the tumor microenvironment and immune-related pathway, is rarely researched in ccRCC. Therefore, we integrated bulk RNA-seq data, DNA mutation and methylation data, single-cell data, and proteomic data to perform a comprehensive analysis of coagulation-related genes in ccRCC. First, we demonstrated the importance of the coagulation-related gene set by consensus clustering. Based on machine learning, we identified 5 coagulation signature genes and verified their clinical value in TCGA, ICGC, and E-MTAB-1980 databases. It's also demonstrated that the specific expression patterns of coagulation signature genes driven by CNV and methylation were closely correlated with pathways including apoptosis, immune infiltration, angiogenesis, and the construction of extracellular matrix. Moreover, we identified two types of tumor cells in single-cell data by machine learning, and the coagulation signature genes were differentially expressed in two types of tumor cells. Besides, the signature genes were proven to influence immune cells especially the differentiation of T cells. And their protein level was also validated.

PGD2/PTGDR2 Signal Affects the Viability, Invasion, Apoptosis, and Stemness of Gastric Cancer Stem Cells and Prevents the Progression of Gastric Cancer

BACKGROUND

Prostaglandin D2 (PGD2) has been shown to restrict the occurrence and development of multiple cancers; nevertheless, its underlying molecular mechanism has not been fully elucidated. The present study investigated the effect of PGD2 on the biological function of the enriched gastric cancer stem cells (GCSCs), as well as its underlying molecular mechanism, to provide a theoretical basis and potential therapeutic drugs for gastric cancer (GC) treatment.

METHODS

The plasma PGD2 levels were detected by Enzyme-linked immunosorbent assay (ELISA). Silencing of lipocalin prostaglandin D synthetases (L-PTGDS) and prostaglandin D2 receptor 2 (PTGDR2) was carried out in GCSCs from SGC-7901 and HGC-27 cell lines. Cell Counting Kit-8, transwell, flow cytometry, and western blotting assays were used to determine cell viability, invasion, apoptosis, and stemness of GCSCs. In vivo xenograft models were used to assess tumor growth.

RESULTS

Clinically, it was found that the plasma PGD2 level decreased significantly in patients with GC. PGD2 suppressed viability, invasion, and stemness and increased the apoptosis of GCSCs. Downregulating L-PTGDS and PTGDR2 promoted viability, invasion, and stemness and reduced the apoptosis of GCSCs. Moreover, the inhibition of GCSCs induced by PGD2 was eliminated by downregulating the expression of PTGDR2. The results of in vivo experiments were consistent with those of in vitro experiments.

CONCLUSION

Our data suggest that PGD2 may be an important marker and potential therapeutic target in the clinical management of GC. L-PTGDS/PTGDR2 may be one of the critical targets for GC therapy. The PGD2/PTGDR2 signal affects the viability, invasion, apoptosis, and stemness of GCSCs and prevents the progression of GC.

From cancer big data to treatment: Artificial intelligence in cancer research

In recent years, developing the idea of "cancer big data" has emerged as a result of the significant expansion of various fields such as clinical research, genomics, proteomics and public health records. Advances in omics technologies are making a significant contribution to cancer big data in biomedicine and disease diagnosis. The increasingly availability of extensive cancer big data has set the stage for the development of multimodal artificial intelligence (AI) frameworks. These frameworks aim to analyze high-dimensional multi-omics data, extracting meaningful information that is challenging to obtain manually. Although interpretability and data quality remain critical challenges, these methods hold great promise for advancing our understanding of cancer biology and improving patient care and clinical outcomes. Here, we provide an overview of cancer big data and explore the applications of both traditional machine learning and deep learning approaches in cancer genomic and proteomic studies. We briefly discuss the challenges and potential of AI techniques in the integrated analysis of omics data, as well as the future direction of personalized treatment options in cancer.

Investigating the Diagnostic and Therapeutic Potential of SREBF2-Related Lipid Metabolism Genes in Colon Cancer

Purpose

Colon cancer is one of the leading causes of death worldwide, and screening of effective molecular markers for the diagnosis is prioritised for prevention and treatment. This study aimed to investigate the diagnostic and predictive potential of genes related to the lipid metabolism pathway, regulated by a protein called sterol-regulatory element-binding transcription Factor 2 (SREBF2), for colon cancer and patient outcomes.

Methods

We used machine-learning algorithms to identify key genes associated with SREBF2 in colon cancer based on a public database. A nomogram was created to assess the diagnostic value of these genes and validated in the Cancer Genome Atlas. We also analysed the relationship between these genes and the immune microenvironment of colon tumours, as well as the correlation between gene expression and clinicopathological characteristics and prognosis in the China Medical University (CMU) clinical cohort.

Results

Three genes, 7-dehydrocholesterol reductase (DHCR7), hydroxysteroid 11-beta dehydrogenase 2 (HSD11B2), and Ral guanine nucleotide dissociation stimulator-like 1 (RGL1), were identified as hub genes related to SREBF2 and colon cancer. Using the TCGA dataset, receiver operating characteristic curve analysis showed the area under the curve values of 0.943, 0.976, and 0.868 for DHCR7, HSD11B2, and RGL1, respectively. In the CMU cohort, SREBF2 and DHCR7 expression levels were correlated with TNM stage and tumour invasion depth (P < 0.05), and high DHCR7 expression was related to poor prognosis of colon cancer (P < 0.05). Furthermore, DHCR7 gene expression was positively correlated with the abundance of M0 and M1 macrophages and inversely correlated with the abundance of M2 macrophages, suggesting that the immune microenvironment may play a role in colon cancer surveillance. There was a correlation between SREBF2 and DHCR7 expression across cancers in the TCGA database.

Conclusion

This study highlights the potential of DHCR7 as a diagnostic marker and therapeutic target for colon cancer.

Risk factors for anastomotic fistula development after radical colon cancer surgery and their impact on prognosis

BACKGROUND

Colon cancer is a common malignant tumor in the gastrointestinal tract that is typically treated surgically. However, postradical surgery is prone to complications such as anastomotic fistulas.

AIM

To investigate the risk factors for postoperative anastomotic fistulas and their impact on the prognosis of patients with colon cancer.

METHODS

We conducted a retrospective analysis of 488 patients with colon cancer who underwent radical surgery. This study was performed between April 2016 and April 2019 at a tertiary hospital in Wuxi, Jiangsu Province, China. A t-test was used to compare laboratory indicators between patients with and those without postoperative anastomotic fistulas. Multiple logistic regression analysis was performed to identify independent risk factors for postoperative anastomotic fistulas. The Functional Assessment of Cancer Therapy-Colorectal Cancer was also used to assess postoperative recovery.

RESULTS

Binary logistic regression analysis revealed that age [odds ratio (OR) = 1.043, P = 0.015], tumor, node, metastasis stage (OR = 2.337, P = 0.041), and surgical procedure were independent risk factors for postoperative anastomotic fistulas. Multiple linear regression analysis showed that the development of postoperative anastomotic fistula (P = 0.000), advanced age (P = 0.003), and the presence of diabetes mellitus (P = 0.015), among other factors, independently affected prognosis.

CONCLUSION

Postoperative anastomotic fistulas significantly affect prognosis and survival rates. Therefore, focusing on the clinical characteristics and risk factors and immediately implementing individualized preventive measures are important to minimize their occurrence.

DLGAP5 Regulates the Proliferation, Migration, Invasion, and Cell Cycle of Breast Cancer Cells via the JAK2/STAT3 Signaling Axis

The aim of this study was to discover new biomarkers to detect breast cancer (BC), which is an aggressive cancer with a high mortality rate. In this study, bioinformatic analyses (differential analysis, weighted gene co-expression network analysis, and machine learning) were performed to identify potential candidate genes for BC to study their molecular mechanisms. Furthermore, Quantitative Real-time PCR and immunohistochemistry assays were used to examine the protein and mRNA expression levels of a particular candidate gene (DLGAP5). And the effects of DLGAP5 on cell proliferation, migration, invasion, and cell cycle were further assessed using the Cell Counting Kit-8 assay, colony formation, Transwell, wound healing, and flow cytometry assays. Moreover, the changes in the JAK2/STAT3 signaling-pathway-related proteins were detected by Western Blot. A total of 44 overlapping genes were obtained by differential analysis and weighted gene co-expression network analysis, of which 25 genes were found in the most tightly connected cluster. Finally, NEK2, CKS2, UHRF1, DLGAP5, and FAM83D were considered as potential biomarkers of BC. Moreover, DLGAP5 was highly expressed in BC. The down-regulation of DLGAP5 may inhibit the proliferation, migration, invasion, and cell cycle of BC cells, and the opposite was true for DLGAP5 overexpression. Correspondingly, silencing or overexpression of the DLGAP5 gene inhibited or activated the JAK2/STAT3 signaling pathway, respectively. DLGAP5, as a potential biomarker of BC, may impact the cell proliferation, migration, invasion, cell cycle, and BC development by modulating the JAK2/STAT3 signaling pathway.

Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.

Machine learning analysis of lung squamous cell carcinoma gene expression datasets reveals novel prognostic signatures

BACKGROUND

Lung squamous cell carcinoma (LUSC) patients are often diagnosed at an advanced stage and have poor prognoses. Thus, identifying novel biomarkers for the LUSC is of utmost importance.

METHODS

Multiple datasets from the NCBI-GEO repository were obtained and merged to construct the complete dataset. We also constructed a subset from this complete dataset with only known cancer driver genes. Further, machine learning classifiers were employed to obtain the best features from both datasets. Simultaneously, we perform differential gene expression analysis. Furthermore, survival and enrichment analyses were performed.

RESULTS

The kNN classifier performed comparatively better on the complete and driver datasets' top 40 and 50 gene features, respectively. Out of these 90 gene features, 35 were found to be differentially regulated. Lasso-penalized Cox regression further reduced the number of genes to eight. The median risk score of these eight genes significantly stratified the patients, and low-risk patients have significantly better overall survival. We validated the robust performance of these eight genes on the TCGA dataset. Pathway enrichment analysis identified that these genes are associated with cell cycle, cell proliferation, and migration.

CONCLUSION

This study demonstrates that an integrated approach involving machine learning and system biology may effectively identify novel biomarkers for LUSC.