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Background/objective: Omics technology has become a widely applied biological science that can be used to study the etiology, pathogenesis, and treatment of osteosarcoma(OS). Bibliometric analysis is still blank in this field.This study aimed to access the trends and hotspots of omics in OS research through the bibliometric analysis method. Methods: Relevant articles and reviews from 1999 to 2023 were retrieved from the Web of Science Core Collection. The data were processed with CiteSpace, and some graphs were generated with Graphpad, VOSviewer, Scimago Graphica, Bibliometrix and R Studio. Results: A total of 1581 papers were included. China (569, 36.0%) and the United States (523, 33.1%) took the dominant position in the number of published papers, and the links between countries most frequently occurred between North America and East Asia, and between Australia and Europe. Top institutions with the highest number of publications were almost located in the United States, with The University of Texas MD Anderson Cancer Center contributing the most (44, 2.78%). Among the researchers in this field, Cleton-Jansen AM was the author with the highest number of articles in the field (20, 1.27%). According to the keyword cluster analysis, most studies focused on the “comparative genomic hybridization” before 2012. The latest surge words “tumor microenvironment” and “immune infiltration” in the keyword heatmap indicate future research directions. Conclusion: Our study provided the current status of the omics research in OS on a global level and the hottest directions. The field of omics in OS was developing rapidly, and the main focuses of research were revealing the characteristics of tumor microenvironment of OS and how to activate the immune system to fight cancer cells. Research on the immune microenvironment and its relationship with genetic aberrations of OS will be a priority in the future.Highlights: The research of omics in osteosarcoma has been exploding since 1998 due to the development of omics technology and the emphasis of researchers. The research hotspots had shifted from the discovering of new molecular target to revealing the characteristics of tumor microenvironment of osteosarcoma and how to activate the immune system to fight cancer cells. According to the present trend, research on the immune microenvironment and its molecular mechanisms will be a priority in the future. [ABSTRACT FROM AUTHOR]

Purpose: There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. Methods: In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018–2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. Results: Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. Conclusion: The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives. [ABSTRACT FROM AUTHOR]

Purpose: Resistance to chemotherapy and radiotherapy is the primary cause of a poor prognosis in oncological patients. Researchers identified many possible mechanisms involved in gaining a therapy-resistant phenotype by cancer cells, including alterations in intracellular drug accumulation, detoxification, and enhanced DNA damage repair. All these features are characteristic of stem cells, making them the major culprit of chemoresistance. This paper reviews the most recent evidence regarding the association between the stemness phenotype and chemoresistance in head and neck cancers. It also investigates the impact of pharmacologically targeting cancer stem cell populations in this subset of malignancies. Methods: This narrative review was prepared based on the search of the PubMed database for relevant papers. Results: Head and neck cancer cells belonging to the stem cell population are distinguished by the high expression of certain surface proteins (e.g., CD10, CD44, CD133), pluripotency-related transcription factors (SOX2, OCT4, NANOG), and increased activity of aldehyde dehydrogenase (ALDH). Chemotherapy itself increases the percentage of stem-like cells. Importantly, the intratumor heterogeneity of stem cell subpopulations reflects cell plasticity which has great importance for chemoresistance induction. Conclusions: Evidence points to the advantage of combining classical chemotherapeutics with stemness modulators thanks to the joint targeting of the bulk of proliferating tumor cells and chemoresistant cancer stem cells, which could cause recurrence. [ABSTRACT FROM AUTHOR]

Introduction: Performing low-dose radiation therapy (LDRT) is a new approach to treat pneumonia resulting from COVID-19 disease. This paper aims to evaluate the effectiveness of LDRT in treating COVID-19 patients. Methods: Medline was searched for "low-dose" and "radiation therapy" and "COVID-19" and "pneumonia" and "inflammation", to retrieve papers that published on low-dose radiation therapy to improve mortality of COVID-19 patients. Only clinical investigations that included original and case report papers were selected for this paper. Results : The completed clinical trials that have performed LDRT to treat COVID-19 showed that the effectiveness of LDRT in treating COVID-19 was up to 90%. Conclusion: The vast majority of primary and secondary outcomes of clinical trial investigations regarding LDRT in treating COVID-19 found that LDRT can be considered a feasible treatment to improve mortality of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Introduction: Feature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the accuracy and efficiency of the system. Since there are many irrelevant and redundant features in breast cancer diagnosis, removing such features leads to more accurate prediction and reduced decision time when dealing with large-scale data. Meanwhile, ensemble classifiers are powerful techniques to improve the prediction performance of classification models, where several individual classifier models are combined to achieve higher accuracy. Methods: In this paper, an ensemble classifier algorithm based on multilayer perceptron neural network is proposed for the classification task, in which the parameters (e.g., number of hidden layers, number of neurons in each hidden layer, and weights of links) are adjusted based on an evolutionary approach. Meanwhile, this paper uses a hybrid dimensionality reduction technique based on principal component analysis and information gain to address this problem. Results: The effectiveness of the proposed algorithm was evaluated based on the Wisconsin breast cancer database. In particular, the proposed algorithm provides an average of 17% better accuracy compared to the best results obtained from the existing state-of-the-art methods. Conclusion: Experimental results show that the proposed algorithm can be used as an intelligent medical assistant system for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) exhibits a substantial morbidity and mortality rate, with its aetiology and pathogenesis remain elusive. It holds significant importance within the tumour microenvironment (TME) and exerts a crucial regulatory influence on tumorigenesis, progression, and metastasis. TAMs possess the capability to foster CRC pathogenesis, proliferation, invasion, and metastasis, as well as angiogenesis, immune evasion, and tumour resistance. Furthermore, TAMs can mediate the prognosis of CRC. In this paper, we review the mechanisms by which natural compounds target TAMs to exert anti-CRC effects from the perspective of the promotional effects of TAMs on CRC, mainly regulating the polarization of TAMs, reducing the infiltration and recruitment of TAMs, enhancing the phagocytosis of macrophages, and regulating the signalling pathways and cytokines, and discuss the potential value and therapeutic strategies of natural compounds-targeting the TAMs pathway in CRC clinical treatment. [ABSTRACT FROM AUTHOR]

Introduction: In recent decades, many theories have been proposed about the cause of hereditary diseases such as cancer. However, most studies state genetic and environmental factors as the most important parameters. It has been shown that gene expression data are valuable information about hereditary diseases and their analysis can identify the relationships between these diseases. Objective: Identification of damaged genes from various diseases can be done through the discovery of cell-to-cell biological communications. Also, extraction of intercellular communications can identify relationships between different diseases. For example, gene disorders that cause damage to the same cells in both breast and blood cancers. Hence, the purpose is to discover cell-to-cell biological communications in gene expression data. Methodology: The identification of cell-to-cell biological communications for various cancer diseases has been widely performed by clustering algorithms. However, this field remains open due to the abundance of unprocessed gene expression data. Accordingly, this paper focuses on the development of a semi-supervised ensemble clustering algorithm that can discover relationships between different diseases through the extraction of cell-to-cell biological communications. The proposed clustering framework includes a stratified feature sampling mechanism and a novel similarity metric to deal with high-dimensional data and improve the diversity of primary partitions. Results: The performance of the proposed clustering algorithm is verified with several datasets from the UCI machine learning repository and then applied to the FANTOM5 dataset to extract cell-to-cell biological communications. The used version of this dataset contains 108 cells and 86,427 promoters from 702 samples. The strength of communication between two similar cells from different diseases indicates the relationship of those diseases. Here, the strength of communication is determined by promoter, so we found the highest cell-to-cell biological communication between “basophils” and “ciliary.epithelial.cells” with 62,809 promoters. Conclusion: The maximum cell-to-cell biological similarity in each cluster can be used to detect the relationship between different diseases such as cancer. [ABSTRACT FROM AUTHOR]

Purpose: This article summarizes natural products that target the MAPK-signaling pathway in cancer therapy. The classification, chemical structures, and anti-cancer mechanisms of these natural products are elucidated, and comprehensive information is provided on their potential use in cancer therapy. Methods: Using the PubMed database, we searched for keywords, including “tumor”, “cancer”, “natural product”, “phytochemistry”, “plant chemical components”, and “MAPK-signaling pathway”. We also screened for compounds with well-defined structures that targeting the MAPK-signaling pathway and have anti-cancer effects. We used Kingdraw software and Adobe Photoshop software to draw the chemical compound structural diagrams. Results: A total of 131 papers were searched, from which 85 compounds with well-defined structures were selected. These compounds have clear mechanisms for targeting cancer treatment and are mainly related to the MAPK-signaling pathway. Examples include eupatilin, carvacrol, oridonin, sophoridine, diosgenin, and juglone. These chemical components are classified as flavonoids, phenols, terpenoids, alkaloids, steroidal saponins, and quinones. Conclusions: Certain MAPK pathway inhibitors have been used for clinical treatment. However, the clinical feedback has not been promising because of genomic instability, drug resistance, and side effects. Natural products have few side effects, good medicinal efficacy, a wide range of sources, individual heterogeneity of biological activity, and are capable of treating disease from multiple targets. These characteristics make natural products promising drugs for cancer treatment. [ABSTRACT FROM AUTHOR]

Introduction: Breast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science as one of the most important issues. In addition to reducing costs, the diagnosis of benign or malignant breast cancer is very important in determining the treatment method. Objective: The purpose of this paper is to present a model based on data mining techniques including feature selection and ensemble classification that can accurately predict breast cancer patients in the early stages. Methodology: The proposed breast cancer detection model is developed by joining Adaptive Differential Evolution (ADE) algorithm for feature selection and Learning Vector Quantization (LVQ) neural network for classification. Our proposed model as ADE–LVQ has the ability to automatically and quickly diagnose breast cancer patients into two classes, benign and malignant. As a new evolutionary approach, ADE performs optimal configuration for LVQ neural network in addition to selecting effective features from breast cancer data. Meanwhile, we configure an ensemble classification technique based on LVQ, which significantly improves the prediction performance. Results: ADE–LVQ has been analyzed from different perspectives on different datasets from Wisconsin breast cancer database. We apply different approaches to handle missing values and improve data quality on this database. The results of the simulations showed that the ADE–LVQ model is more successful than the equivalent and state-of-the-art models in diagnosing breast cancer patients. Also, ADE–LVQ provides better performance with less complexity, considering feature selection and ensemble learning. In particular, ADE–LVQ improves accuracy (up to 3.4%) and runtime (up to 2.3%) on average compared to the existing best method. Conclusion: Combined methods based on data mining techniques for breast cancer diagnosis can help doctors in making better decisions for disease treatment. [ABSTRACT FROM AUTHOR]

Purpose: The morphology of bone marrow cells is essential in identifying malignant hematological disorders. The automatic classification model of bone marrow cell morphology based on convolutional neural networks shows considerable promise in terms of diagnostic efficiency and accuracy. However, due to the lack of acceptable accuracy in bone marrow cell classification algorithms, automatic classification of bone marrow cells is now infrequently used in clinical facilities. To address the issue of precision, in this paper, we propose a Dual Attention Gates DenseNet (DAGDNet) to construct a novel efficient, and high-precision bone marrow cell classification model for enhancing the classification model's performance even further. Methods: DAGDNet is constructed by embedding a novel dual attention gates (DAGs) mechanism in the architecture of DenseNet. DAGs are used to filter and highlight the position-related features in DenseNet to improve the precision and recall of neural network-based cell classifiers. We have constructed a dataset of bone marrow cell morphology from the First Affiliated Hospital of Chongqing Medical University, which mainly consists of leukemia samples, to train and test our proposed DAGDNet together with the bone marrow cell classification dataset. Results: When evaluated on a multi-center dataset, experimental results show that our proposed DAGDNet outperforms image classification models such as DenseNet and ResNeXt in bone marrow cell classification performance. The mean precision of DAGDNet on the Munich Leukemia Laboratory dataset is 88.1%, achieving state-of-the-art performance while still maintaining high efficiency. Conclusion: Our data demonstrate that the DAGDNet can improve the efficacy of automatic bone marrow cell classification and can be exploited as an assisting diagnosis tool in clinical applications. Moreover, the DAGDNet is also an efficient model that can swiftly inspect a large number of bone marrow cells and offers the benefit of reducing the probability of an incorrect diagnosis. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a prevalent form of cancer, with Helicobacter pylori (H. pylori) infection being the most common risk factor. Recent studies have highlighted the role of long-term irritation of the gastric mucosa caused by bile reflux in the development of cancer. Bile acids (BAs), which are a significant component in bile reflux, have the potential to promote gastric carcinogenesis through various mechanisms. These mechanisms include the induction of intestinal metaplasia (IM), inhibition of H. pylori activity, modification of H. pylori colonization, and alteration of the abundance and composition of microorganisms in the stomach. Defining the mechanism of bile acid-induced gastric carcinogenesis could potentially be an effective approach to prevent GC. Hence, this paper aims to review the mechanism of bile acid-induced IM, the association between BAs and H. pylori infection as well as microorganisms in the stomach, and the correlation between BAs and gastric carcinogenesis. The ultimate goal is to elucidate the role of BAs in the development of GC. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in women worldwide. Early diagnosis and treatment are the key for a favorable prognosis. The application of artificial intelligence technology in the medical field is increasingly extensive, including image analysis, automated diagnosis, intelligent pharmaceutical system, personalized treatment and so on. AI-based breast cancer imaging, pathology and adjuvant therapy technology cannot only reduce the workload of clinicians, but also continuously improve the accuracy and sensitivity of breast cancer diagnosis and treatment. This paper reviews the application of AI in breast cancer, as well as looks ahead and poses challenges to the future development of AI for breast cancer detection and therapeutic, so as to provide ideas for future research. [ABSTRACT FROM AUTHOR]

Purpose: Skin disease is a prevalent type of physical ailment that can manifest in multitude of forms. Many internal diseases can be directly reflected on the skin, and if left unattended, skin diseases can potentially develop into skin cancer. Accurate and effective segmentation of skin lesions, especially melanoma, is critical for early detection and diagnosis of skin cancer. However, the complex color variations, boundary ambiguity, and scale variations in skin lesion regions present significant challenges for precise segmentation. Methods: We propose a novel approach for melanoma segmentation using a dual-branch interactive U-Net architecture. Two distinct sampling strategies are simultaneously integrated into the network, creating a vertical dual-branch structure. Meanwhile, we introduce a novel dual-channel symmetrical convolution block (DCS-Conv), which employs a symmetrical design, enabling the network to exhibit a horizontal dual-branch structure. The combination of the vertical and horizontal distribution of the dual-branch structure enhances both the depth and width of the network, providing greater diversity and rich multiscale cascade features. Additionally, this paper introduces a novel module called the residual fuse-and-select module (RFS module), which leverages self-attention mechanisms to focus on the specific skin cancer features and reduce irrelevant artifacts, further improving the segmentation accuracy. Results: We evaluated our approach on two publicly skin cancer datasets, ISIC2016 and PH2, and achieved state-of-the-art results, surpassing previous outcomes in terms of segmentation accuracy and overall performance. Conclusion: Our proposed approach holds tremendous potential to aid dermatologists in clinical decision-making. [ABSTRACT FROM AUTHOR]

Introduction: Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning approaches cannot replace professional humans, but they can change the treatment of diseases such as cancer and be used as medical assistants. Background: Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. Feature selection and classification are common data mining techniques in machine learning that can provide breast cancer diagnosis with high speed, low cost and high precision. Methodology: This paper proposes a new intelligent approach using an integrated filter-evolutionary search-based feature selection and an optimized ensemble classifier for breast cancer diagnosis. The selected features mainly relate to the viable solution as the selected features are successfully used in the breast cancer disease classification process. The proposed feature selection method selects the most informative features from the original feature set by integrating adaptive thresholder information gain-based feature selection and evolutionary gravity-search-based feature selection. Meanwhile, classification model is done by proposing a new intelligent multi-layer perceptron neural network-based ensemble classifier. Results: The simulation results show that the proposed method provides better performance compared to the state-of-the-art algorithms in terms of various criteria such as accuracy, sensitivity and specificity. Specifically, the proposed method achieves an average accuracy of 99.42% on WBCD, WDBC and WPBC datasets from Wisconsin database with only 56.7% of features. Conclusion: Systems based on intelligent medical assistants configured with machine learning approaches are an important step toward helping doctors to detect breast cancer early. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the significant diseases that threaten the survival of men worldwide, with the progression of androgen deprivation therapy, become much rely on it, finally, developed into castration-resistant prostate cancer (ADT). In western countries, ranks second in incidence, and in China, with increasing lifespan, the incidence of prostate cancer is rising steadily. Although chemotherapy agents, such as taxane, have achieved some efficacy, treatment failure still occur. As sensitivity of hormone levels change, the disease can progress to castrate-resistant prostate cancer. Because of the poor efficacy of traditional surgery, endocrine therapy, radiation therapy, and chemotherapy, the treatment options for castrate-resistant prostate cancer are limited. Advanced prostate cancer can progress on immunotherapy, and thus, bio -immunotherapy targeting the unique, prostate microenvironment is an important option. In this paper, we systematically revealed the role of three types of bio-immunotherapies (immune checkpoint inhibitors, tumors, vaccines, cytokines) in castrate-resistant prostate cancer, providing a reference for clinical treatment of prostate cancer. [ABSTRACT FROM AUTHOR]

Purpose: Cancer-associated fibroblasts (CAFs) are one of the most critical cells in the tumor environment, with crucial roles in cancer progression and metastasis. Due to Field-Effect phenomena (also called field cancerization), the adjacent cavity side area of the margin is histologically normal, but it has been entered into neoplastic transformation due to MCT4 and MCT1 pathways activated by H2O2/ROS oxidative stress agents secreted by CAF in adjacent tumor bed microenvironment. This paper specifically focused on the role of cancer-associated fibroblast in breast tumor beds and its correlation with the presence of scattered cancer cells or onco-protein-activated cells (may be high risk but not completely transformed cancer cells) in the cavity side margins. Methods: In this study, the glycolytic behavior of non-tumoral cavity side margins was examined using carbon nanotube-based electrochemical biosensors integrated into a cancer diagnostic probe. This method enabled the detection of CAF accumulation sites in non-cancerous neighboring tissues of tumors, with a correlation to CAF concentration. Subsequently, RT-PCR, fluorescent, histopathological, and invasion assays were conducted on hyperglycolytic lesions to explore any correlation between the abundance of CAFs and the electrochemical responses of the non-cancerous tissues surrounding the tumor, as well as their neoplastic potential. Results: We observed overexpression of cancer-associated transcriptomes as well as the presence and hyperactivation of CAFs in cavity-side regions in which glycolytic metabolism was recorded, independent of the histopathological state of the lesion. At mean 70.4%, 66.7%, 70.4%, and 44.5% increments were observed in GLUT-1, MMP-2, N-cadherin, and MMP-9 transcriptomes by highly glycolytic but histologically cancer-free expression samples in comparison with negative controls (histologically non-cancer lesions with low glycolytic behavior). Conclusion: The presence of CAFs is correlated with the presence of high glycolytic metabolism in the cavity margin lesion, high ROS level in the lesion, and finally aggressive cancer-associated proteins (such as MMP2, …) in the margin while these metabolomes, molecules, and proteins are absent in the margins with negatively scored CDP response and low ROS level. So, it seems that when we observe CAFs in glycolytic lesions with high ROS levels, some high-risk epithelial breast cells may exist while no histological trace of cancer cells was observed. Further research on CAFs could provide valuable insights into the local recurrence of malignant breast diseases. Hence, real-time sensors can be used to detect and investigate CAFs in the non-tumoral regions surrounding tumors in cancer patients, potentially aiding in the prevention of cancer recurrence. [ABSTRACT FROM AUTHOR]

Background: Tumor is a new organism formed by abnormal hyperplasia of local tissue cells under the action of various tumorigenic factors. Inflammation plays a decisive role in inducing tumorigenesis, promoting tumor development, invasion and migration. More and more evidence indicate that exosomes are involved in regulating the formation of tumor microenvironment in the process of proinflammatory carcinogenesis, leading to the stimulation of anti-tumor immune response or systemic immunosuppression, and exosomes play a crucial role in the development of tumor. Methods: The articles on tumor-derived exosomes and inflammatory responses from January 2005 to January 2024 were collected through Web of Science (WOS), and the inclusion criteria were “Article”, “Review Article” and “Early Access”. Articles obtained after excluding “Book Chapters”, “Editorial Material”, “Proceeding Paper”, “Meeting Abstract” and “Retracted Publication”. Bibliometrics and visualization analysis were carried out on the obtained articles using CiteSpace6.2.R6 and VOSviewer1.6.20. Results: Total of 703 articles were included. The number of published documents showed a fluctuating growth trend year by year. A total of 61 countries have participated in the research on the effects of exosomes and inflammatory responses on tumors, among which China and the United States have the largest influence in this field. The obtained articles have been published in 60 journals around the world, among which PLOS ONE and NAT REV IMMUNOL are the journals with the most published articles and the highest co-citations respectively. The article from French author THERY C was cited the most (202 times). As a major researcher on the basic function of exosomes, THERY C established the gold standard for extraction, separation and identification of exosomes, and found that exosomes promote tumor metastasis through direct regulation of miRNA. Her research has had a huge impact on the field. Keyword co-occurrence analysis indicate that extracellular vesicles, inflammation, cancer, miRNAs, mesenchymal stem cells, drug delivery, gastric cancer and circulating endothelial microparticles are the research hotspot at present stage. The main keywords of the cluster analysis show that extracellular vesicles, human papilloma virus, myeloid cells, tumor macro-environment are the current research hotspots and frontier. The research hotspots have developed over time from the time chart of keywords and clustering, especially after 2016, exosomes have established extensive links with drug delivery, cancer treatment, inflammatory response and other fields. Tumor-derived exosomes stimulate receptor cells to secrete pro-inflammatory cytokines and growth factors, enabling immune and inflammatory cells to perceive the intracellular environment of cancer cells even when cancer cells do not express any tumor-specific antigens. For example, in anoxic environment, cancer cells can secrete exosomes containing pro-inflammatory factors to promote the invasion and metastasis of cancer cells. In the complex tumor microenvironment, both tumor cells and various stromal cells will secrete specific exosomes, and promote the development of tumors through various ways, so that tumor cells have drug resistance, and bring adverse effects on the clinical treatment of tumor patients. MicroRNAs and long noncoding RNA as hot keywords play important roles in regulating and mediating tumor development, and their specificity makes them important biomarkers for cancer prediction and diagnosis. Highlighting word analysis shows that microRNAs secreted by leukemia patients can effectively promote the proliferation of malignant cells and the development of cardiovascular diseases. At the same time, exosomes can induce the secretion of some microRNAs in patients, leading to cardiac repair and regeneration. Therefore, the detection and screening of microRNAs plays a crucial role in predicting the incidence of cardiovascular diseases in patients. Conclusion: Exosomes have attracted increasing attention due to their significant heterogeneity and ability to regulate the tumor immune microenvironment. However, tumor cell-derived exosomes accelerate tumor progression by enhancing immunosuppression and inflammation, increasing oxidative stress, and promoting angiogenesis, which may lead to poor prognosis. [ABSTRACT FROM AUTHOR]

Lung cancer creates pulmonary nodules in the patient's lung, which may be diagnosed early on using computer-aided diagnostics. A novel automated pulmonary nodule diagnosis technique using three-dimensional deep convolutional neural networks and multi-layered filter has been presented in this paper. For the suggested automated diagnosis of lung nodule, volumetric computed tomographic images are employed. The proposed approach generates three-dimensional feature layers, which retain the temporal links between adjacent slices of computed tomographic images. The use of several activation functions at different levels of the proposed network results in increased feature extraction and efficient classification. The suggested approach divides lung volumetric computed tomography pictures into malignant and benign categories. The suggested technique's performance is evaluated using three commonly used datasets in the domain: LUNA 16, LIDC-IDRI, and TCIA. The proposed method outperforms the state-of-the-art in terms of accuracy, sensitivity, specificity, F-1 score, false-positive rate, false-negative rate, and error rate. [ABSTRACT FROM AUTHOR]

Introduction: Epidemiological studies show that breast cancer is the most common cancer in women in the world. Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. The goal can be achieved by using large-scale breast cancer data with the machine learning models Methods: This paper proposes a new intelligent approach using an optimized ensemble classifier for breast cancer diagnosis. The classification is done by proposing a new intelligent Group Method of Data Handling (GMDH) neural network-based ensemble classifier. This method improves the performance of the machine learning technique by using a Teaching–Learning-Based Optimization (TLBO) algorithm to optimize the hyperparameters of the classifier. Meanwhile, we use TLBO as an evolutionary method to address the problem of appropriate feature selection in breast cancer data. Results: The simulation results show that the proposed method has a better accuracy between 7 and 26% compared to the best results of the existing equivalent algorithms. Conclusion: According to the obtained results, we suggest the proposed algorithm as an intelligent medical assistant system for breast cancer diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: Guidelines recommend a structured symptom screening (SC) for especially advanced cancer patients (CPs). The aim of this multicenter German prospective quality assurance project KeSBa (Kennzahl Symptom- und Belastungserfassung) was to gain knowledge on SC procedures in Oncology Centers (OCs) for advanced cancer patients and a first impression on the consequences of SC. Methods: The KeSBa project consisted of three phases: pilot, 3 months screening and feedback phase. Participating OCs decided to use either the Minimal Documentation System (MIDOS) or the Integrated Palliative care Outcome Scale (IPOS) and defined the cutoff values for positive screening results. Results: Out of 172 certified German OCs, 40 (23%) participated in the KeSBa pilot phase, 29 (16.8%) in the 3 months screening phase using MIDOS (n = 18, 58.6%) or IPOS (n = 11, 41.3%) and in the feedback round. 25/29 performed paper-based screening (86.2%). 2.963 CPs were screened. Results were documented for 1255 (42.2%, SC +) positive and 874 (29.5%, SC–) negative screenings depending on the center´s schedules: 452 SC + CPs (28.4%) and 42 SC– CPs (2.6%) had contact to specialized palliative care or other supportive specialist teams afterwards, 458 SC + CPs (28.8%) and 605 SC– CPs (38.1%) remained in standard oncology care. In the feedback round missing resources (personal and IT) and improved communication were mentioned most often. Conclusion: Routine SC is feasible in advanced CPs treated in OCs but associated with considerable workload. In 42.2% of CPs SC was classified as positive, indicating the need of further diagnostics or professional judgment. SC requires staff and IT resources. [ABSTRACT FROM AUTHOR]

Purpose: Immune checkpoint inhibition therapy (ICIT) is an emerging field in oncology especially opening new horizons to chemotherapy refractory patients. However, immune-related adverse events (irAEs) and undesired response patterns such as progression after the initial good response in a subset of patients pose a major challenge and drawback to ICIT. This paper provides deep insight into ICIT related bottlenecks and corresponding effective management and combat strategies for very complex complications. Methods: The relevant literatures from PubMed have been reviewed. Based on obtained information, rigorous and exhaustive analyses have been made to present novel methods and strategies against ICIT drawbacks and bottlenecks. Results: The results show that baseline biomarker tests are very crucial to identify suitable candidates for ICIT and frequent assessments throughout ICIT help to recognize possible irAEs at early stages. Equally important are the necessity for mathematical definitions for the ICIT success rate and optimum duration, and the development of combat mechanisms against loss of sensitivity within the tumor microenvironment (TME). Conclusion: Rigorous management approaches are presented for mostly observed irAEs. Furthermore, for the first time in the literature, a non-linear mathematical model is invented to measure the ICIT success rate and to decide about the optimum ICIT duration. Finally, a strategy against tumor plasticity is introduced. [ABSTRACT FROM AUTHOR]

Background: Protocadherin 10 (PCDH 10), a member of the superfamily of protocadherins, is a Ca2+-dependent homophilic cell-cell adhesion molecule expressed on the surface of cell membranes. Protocadherin 10 plays a critical role in the central nervous system including in cell adhesion, formation and maintenance of neural circuits and synapses, regulation of actin assembly, cognitive function and tumor suppression. Additionally, Pcdh10 can serve as a non-invasive diagnostic and prognostic indicator for various cancers. Methods: This paper collects and reviews relevant literature in Pubmed. Conclusion: This review describes the latest research understanding the role of Pcdh10 in neurological disease and human cancer, highlighting the importance of scrutinizing its properties for the development of targeted therapies and identifying a need for further research to explore Pcdh10 functions in other pathways, cell types and human pathologies. [ABSTRACT FROM AUTHOR]

Purpose: Pancreatic ductal adenocarcinoma (PDAC) remains a common and difficult cancer to treat. Surgical resection and chemotherapy are standard of care and clinical outcomes remain poor. Oncolytic adenoviruses are a unique approach to the treatment of this challenging cancer, aiming to overcome the features of this disease that pose the key obstacles to standard therapies. This paper provides a detailed review of the clinical trials of conditionally-replicative adenoviruses in pancreatic cancer to date, with a brief summary of the past preclinical literature and future prospects of this therapy. Methods: MEDLINE, Embase, and clinicaltrials.gov were searched from inception to December 23rd 2022 for clinical trials of conditionally-replicative adenoviruses used in patients with pancreatic ductal adenocarcinoma. Primary features for review included patient demographics, treatment protocol including dose and administration route, adverse events, patient responses and survival rates. Results: The six published clinical trials suggest that objective clinical responses can be achieved with a tolerable level of side effects, even at high viral doses. The more clinically adaptable intravenous route of administration also appears to be as well tolerated as the more challenging intratumoural injections. Conclusion: Published clinical trials provide data of the safety and some signs of oncolytic activity of conditionally-replicative adenoviruses in patients with pancreatic cancer. Importantly, on the latest trials, the easier intravenous route of administration seems to be well tolerated and safe, providing the opportunity for further clinical evaluation. It is hoped that the ongoing clinical trials will yield more promising results of this therapeutic approach against a currently intractable disease. [ABSTRACT FROM AUTHOR]

Purpose: The WHO 2016 re-classification of myeloproliferative neoplasms resulted in a separation of essential thrombocythemia (ET) from the pre-fibrotic and fibrotic (overt) phases of primary myelofibrosis (MF). This study reports on a chart review conducted to evaluate the real life approach regarding clinical characteristics, diagnostic assessment, risk stratification and treatment decisions for MPN patients classified as ET or MF after implementation of the WHO 2016 classification. Methods: In this retrospective chart review, 31 office-based hematologists/oncologists and primary care centers in Germany participated between April 2021 and May 2022. Physicians reported available data obtained from patient charts via paper–pencil based survey (secondary use of data). Patient features were evaluated using descriptive analysis, also including diagnostic assessment, therapeutic strategies and risk stratification. Results: Data of 960 MPN patients diagnosed with essential thrombocythemia (ET) (n = 495) or myelofibrosis (MF) (n = 465) after implementation of the revised 2016 WHO classification of myeloid neoplasms was collected from the patient charts. While they met at least one minor WHO-criteria for primary myelofibrosis, 39.8% of those diagnosed with ET did not have histological BM testing at diagnosis. 63.4% of patients who were classified as having MF, however, did not obtain an early prognostic risk assessment. More than 50% of MF patients showed characteristics consistent with the pre-fibrotic phase, which was emphasized by the frequent use of cytoreductive therapy. Hydroxyurea was the most frequently used cytoreductive medication in 84.7% of ET and 53.1% of MF patients. While both ET and MF cohorts showed cardiovascular risk factors in more than 2/3 of the cases, the use of platelet inhibitors or anticoagulants varied between 56.8% in ET and 38.1% in MF patients. Conclusions: Improved histopathologic diagnostics, dynamic risk stratification including genetic risk factors for cases of suspected ET and MF are recommended for precise risk assessment and therapeutic stratification according to WHO criteria. [ABSTRACT FROM AUTHOR]

Purpose: Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients. Methods: In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA. Results: Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001). Conclusion: In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD. [ABSTRACT FROM AUTHOR]

Background: Osteosarcoma is a common primary malignant tumour of the bone that usually occurs in children and adolescents. It is characterised by difficult treatment, recurrence and metastasis, and poor prognosis. Currently, the treatment of osteosarcoma is mainly based on surgery and auxiliary chemotherapy. However, for recurrent and some primary osteosarcoma cases, owing to the rapid progression of disease and chemotherapy resistance, the effects of chemotherapy are poor. With the rapid development of tumour-targeted therapy, molecular-targeted therapy for osteosarcoma has shown promise. Purpose: In this paper, we review the molecular mechanisms, related targets, and clinical applications of targeted osteosarcoma therapy. In doing this, we provide a summary of recent literature on the characteristics of targeted osteosarcoma therapy, the advantages of its clinical application, and development of targeted therapy in future. We aim to provide new insights into the treatment of osteosarcoma. Conclusion: Targeted therapy shows potential in the treatment of osteosarcoma and may offer an important means of precise and personalised treatment in the future, but drug resistance and adverse effects may limit its application. [ABSTRACT FROM AUTHOR]

Cancer is a disease in which some cells of the body grow uncontrollably and occasionally spread to other parts of the body. With a group of more than 100 different types, cancer can start almost anywhere in the body. Defective cells may form a mass called a tumor which can be cancerous (malignant), which grows and spreads to other parts of the body, or benign that can grow but not spread throughout the body. In 2021, more than 10 million people died of cancer worldwide (1 out of 6 deaths). This paper has thoroughly investigated the cancer status in the Occupied Palestinian Territories (OPT), in terms of its various types; incidence; mortality; sex, age, and geography distribution; and potential causes. In the OPT, with a population of 5.35 million, cancer mortality was 14% in 2016, being the second cause of death after cardiovascular diseases accounting 30.6% of all causes of death. Cancer mortality in the OPT increased by 136% from 2000 to 2016, and by 14% from 2016 to 2020. In addition to other types of cancer in the OPT, its main types are lung (highest in males), breast (highest in females), colorectal (highest in both sexes), and leukemia (highest in children). The high rates of different types of cancer in the OPT can be attributed to various causes, including those related to environmental pollution, nutrition, stress, and lifestyle factors (smoking, lack of activity, increased dependence on technologies, etc.), whereas only 10–30% of cancer cases are attributed to genetics. [ABSTRACT FROM AUTHOR]

Purpose: Omics data are crucial for medical diagnosis as it contains intrinsic biomedical information. Multi-omics integrated analysis has become a new direction for scientists to explore life mechanisms. Nevertheless, the quality of complex omics data often varies greatly due to different samples or even different omics types, it is challenging to dynamically capture the uncertainty for different kinds of omics data. Methods: This paper proposes a uncertainty-aware dynamic integration framework for multi-omics classification. The framework consists of three modules: deep embedding, confidence prediction, and downstream tasks. The deep embedding module extract key information from multi-omics data to obtain a low-dimensional feature representation which is used to train downstream tasks. Combined with the deep embedding module, the confidence prediction module is used to dynamically capture the uncertainty of the data. We introduce "confidNet" to assign a confidence value for each type of omics data, which is used for dynamic integration between multi-omics. Results: Compared with other integration methods, the proposed method can contain more crucial biomedical information in the obtained low-dimensional representation. Our framework realizes reliable integration among multiple omics, and it can still achieve high accuracy on small sample data sets. We have verified the effectiveness of the model in a large number of experiments. Conclusion: Our framework can be widely applied to high-dimensional omics data and has great potential to facilitate medical decision-making and biological analysis. [ABSTRACT FROM AUTHOR]

Background: Cancer patients constitute one of the highest-risk patient groups during the COVID-19 pandemic. In this study, it was aimed to perform a systematic review and meta-analysis to determine both the incidence and ICU (Intensive Care Unit) admission rates and mortality in SARS-CoV-2 infected cancer patients. Methods: The PRISMA guidelines were closely followed during the design, analysis, and reporting of this systematic review and meta-analysis. A comprehensive literature search was performed for the published papers in PubMed/Medline, Scopus, medRxiv, Embase, and Web of Science (WoS) databases. SARS-CoV-2 infection pooled incidence in the cancer populations and the risk ratio (RR) of ICU admission rates/mortality in cancer and non-cancer groups, with 95% confidence intervals (CIs), were calculated using the random-effects model. Results: A total of 58 studies, involving 709,908 participants and 31,732 cancer patients, were included in this study. The incidence in cancer patients was calculated as 8% (95% CI: 8–9%). Analysis results showed that mortality and ICU admission rate was significantly higher in patients with cancer (RR = 2.26, 95% CI: 1.94–2.62, P < 0.001; RR = 1.45, 95% CI: 1.28–1.64, p < 0.001, respectively). Conclusion: As a result, cancer was an important comorbidity and risk factor for all SARS-CoV-2 infected patients. This infection could result in severe and even fatal events in cancer patients. Cancer is associated with a poor prognosis in the COVID-19 pandemic. Cancer patients should be assessed more sensitively in the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Background: Liver cancer (LC) is a prevalent malignancy and a leading cause of cancer-related mortality worldwide. Extensive research has been conducted to enhance patient outcomes and develop effective prevention strategies, ranging from molecular mechanisms to clinical interventions. Single-cell sequencing, as a novel bioanalysis technology, has significantly contributed to the understanding of the global cognition and dynamic changes in liver cancer. However, there is a lack of bibliometric analysis in this specific research area. Therefore, the objective of this study is to provide a comprehensive overview of the knowledge structure and research hotspots in the field of single-cell sequencing in liver cancer research through the use of bibliometrics. Method: Publications related to the application of single-cell sequencing technology to liver cancer research as of December 31, 2023, were searched on the web of science core collection (WoSCC) database. VOSviewers, CiteSpace, and R package “bibliometrix” were used to conduct this bibliometric analysis. Results: A total of 331 publications from 34 countries, primarily led by China and the United States, were included in this study. The research focuses on the application of single cell sequencing technology to liver cancer, and the number of related publications has been increasing year by year. The main research institutions involved in this field are Fudan University, Sun Yat-Sen University, and the Chinese Academy of Sciences. Frontiers in Immunology and Nature Communications is the most popular journal in this field, while Cell is the most frequently co-cited journal. These publications are authored by 2799 individuals, with Fan Jia and Zhou Jian having the most published papers, and Llovet Jm being the most frequently co-cited author. The use of single cell sequencing to explore the immune microenvironment of liver cancer, as well as its implications in immunotherapy and chemotherapy, remains the central focus of this field. The emerging research hotspots are characterized by keywords such as 'Gene-Expression', 'Prognosis', 'Tumor Heterogeneity', 'Immunoregulation', and 'Tumor Immune Microenvironment'. Conclusion: This is the first bibliometric study that comprehensively summarizes the research trends and developments on the application of single cell sequencing in liver cancer. The study identifies recent research frontiers and hot directions, providing a valuable reference for researchers exploring the landscape of liver cancer, understanding the composition of the immune microenvironment, and utilizing single-cell sequencing technology to guide and enhance the prognosis of liver cancer patients. [ABSTRACT FROM AUTHOR]

Background: One popular and well-established marker for the immune checkpoint blockade (ICB) response is tumor mutation burden (TMB). Persistent TMB (pTMB), a subset of TMB, provides a better indicator to predict patient ICB therapy outcomes, as shown by some studies. Immune checkpoint drugs have significantly changed how melanoma is treated in recent years. Methods: In this study, we integrated the TCGA-SKCM database and data of pTMB of TCGA from the paper that first mentioned pTMB and analyzed mutational and Immune characteristics associated with pTMB level in SKCM. Next, the predictive DEGs were identified the subgroups of pTMB by Cox regression and LASSO analyses to construct a pTMB-related signature. Finally, the expression and Biological functions of signature genes was detected, and further validated in vitro assay. Results: In the current research, we explored the mutational and immunological features related to the level of TMB in cutaneous melanoma (CM). The high-pTMB subgroup exhibited an increasing incidence of gene changes and higher levels of immune cell infiltration. Subsequently, we established a pTMB-related signature based on the predictive DEGs and found the biological features and immune-associated variables between two distinct risk groups. Lastly, the results of the clinical sample validation demonstrated that the expression of IL17REL was down-regulated in the collected samples of individuals with CM. The in vitro assay results indicated that IL17REL effectively suppressed the proliferation, clonality, and migration of CM cells. Conclusion: In conclusion, we have developed a prediction model associated with TMB and subsequently validated the potential influence of IL17REL on Overall Survival (OS) in patients diagnosed with melanoma. [ABSTRACT FROM AUTHOR]

Colitis-associated colorectal cancer has been a hot topic in public health issues worldwide. Numerous studies have demonstrated the significance of myeloid-derived suppressor cells (MDSCs) in the progression of this ailment, but the specific mechanism of their role in the transformation of inflammation to cancer is unclear, and potential therapies targeting MDSC are also unclear. This paper outlines the possible involvement of MDSC to the development of colitis-associated colorectal cancer. It also explores the immune and other relevant roles played by MDSC, and collates relevant targeted therapies against MDSC. In addition, current targeted therapies for colorectal cancer are analyzed and summarized. [ABSTRACT FROM AUTHOR]

Background: Glioma is the most common malignant tumor in the central nervous system. In patients with glioma, the prognosis is poor and median survival is only 12–15 months. With the recent development of sequencing technology, important roles of noncoding RNAs are being discovered in cells, especially those of circular RNAs (circRNAs). Because circRNAs are stable, abundant, and highly conserved, they are regarded as novel biomarkers in the early diagnosis and prognosis of diseases. Purpose: In this review, roles and mechanisms of circRNAs in the development of glioma are summarized. Methods: This paper collects and reviews relevant PubMed literature. Conclusion: Several classes of circRNAs are highly expressed in glioma and are associated with malignant biological behaviors of gliomas, including proliferation, migration, invasion, apoptosis, angiogenesis, and drug resistance. Further studies are needed to clarify the roles of circRNAs in glioma and to determine whether it is possible to increase therapeutic effects on tumors through circRNA intervention. [ABSTRACT FROM AUTHOR]

Introduction: The fecal volatolome, which is composed of fecal volatile organic compounds (VOCs), seems to hold potential as non-invasive biomarker for the detection of colorectal cancer (CRC) and its precursor lesions advanced adenomas (AA). The potential of the fecal volatolome has been subject of various studies using either chemical analytical or pattern-recognition techniques. The available literature on the potential of the fecal volatolome as CRC and AA biomarker was reviewed.Methods: A systematic literature search was conducted in PubMed, Embase, the Cochrane Library, Google Scholar and ResearchGate using the following keywords: Colorectal Cancer, Advanced Adenoma, Volatile Organic Compound, Metabolome, Gas Chromatrography-Mass Spectrometry, Selected-Ion Flow-Tube Mass Spectrometry, eNose, and Fecal Biomarkers.Results: Eighty-eight titles or abstracts were identified from the search, of which 11 papers describing the potential of the fecal volatolome for CRC detection were selected. In these studies, different techniques were used for the headspace analyses of fecal VOCs, limiting the possibility to compare outcomes. Increased levels of amino acids and short chain fatty acids, and decreased levels of bile acids and polyol alcohols in the gas phase of feces were observed repeatedly. All selected papers reported high diagnostic value for the detection of both CRC and AA based on fecal VOCs.Conclusion: Based on the included studies, fecal VOC analyses seem promising for future screening of CRC and AA, with potentially improved test performances allowing for earlier detection of AA and CRC and consequently earlier initiation of treatment, possibly reducing morbidity and mortality rates next to lower rates of (unnecessary) colonoscopies. [ABSTRACT FROM AUTHOR]

Purpose: Endometrial cancer in recent years has taken the lead among cancer processes of the female reproductive system. The feasibility of pelvic and para-aortic lymph node dissection in patients with endometrial cancer has always been a controversial issue. The aim of the presented paper is to evaluate the feasibility of pelvic and para-aortic lymph node dissection in patients with endometrial cancer, depending on the stage of the disease, postoperative complications, and patient survival, depending on the volume of surgical intervention. Methods: The study involved 285 patients with stages of I–IV endometrioid endometrial cancer of the Pre-graduate Department of Oncogynecology of the National Cancer Institute. The average age of patients was 55 ± 5.7 years. In 74.5%, the disease was detected at stage I and uterine extirpation was performed with/without appendages. Results: The duration of the operation varies depending on the volume of intervention—from 1 h 30 min ± 10 min for panhysterectomy, up to 3 h 20 min ± 10 min when performing para-aortic lymph node dissection. The average number of lymph nodes removed was—7 ± 1.1 pelvic and 12 ± 1.5 para-aortic. Conclusion: The basic principles of surgical treatment consist in individual choice of the scope of surgical intervention, performing adequate lymph node dissection, and preventing relapse and metastasis of the disease. [ABSTRACT FROM AUTHOR]

Purpose: This paper reviews marine compounds that target the mitogen-activated protein kinase (MAPK) signaling pathway and their main sources, chemical structures, major targeted cancers and possible mechanisms to provide comprehensive and basic information for the development of marine compound-based antitumor drugs in clinical cancer therapy research. Methods: This paper searched the PubMed database using the keywords "cancer", "marine*" and "MAPK signaling pathway"; this search was supplemented by the literature-tracing method. The marine compounds screened for review in this paper are pure compounds with a chemical structure and have antitumor effects on more than one tumor cell line by targeting the MAPK signaling pathway. The PubChem database was used to search for the PubMed CID and draw the chemical structures of the marine compounds. Results: A total of 128 studies were searched, and 32 marine compounds with unique structures from extensive sources were collected for this review. These compounds are cytotoxic to cancer cell lines, although their targets are still unclear. This paper describes their anticancer effect mechanisms and the protein expression changes in the MAPK pathway induced by these marine compound treatments. This review is the first to highlight MAPK signaling pathway-targeted marine compounds and their use in cancer therapy. Conclusion: The MAPK signaling pathway is a promising potential target for cancer therapy. Searching for marine compounds that exert anticancer effects by targeting the MAPK signaling pathway and developing them into new marine anticancer drugs will be beneficial for cancer treatment. [ABSTRACT FROM AUTHOR]

Purpose: Renal cell carcinoma is an aggressive disease with a high mortality rate. Management has drastically changed with the new era of immunotherapy, and novel strategies are being developed; however, identifying systemic treatments is still challenging. This paper presents an update of the expert panel consensus from the Latin American Cooperative Oncology Group and the Latin American Renal Cancer Group on advanced renal cell carcinoma management in Brazil. Methods: A panel of 34 oncologists and experts in renal cell carcinoma discussed and voted on the best options for managing advanced disease in Brazil, including systemic treatment of early and metastatic renal cell carcinoma as well as nonclear cell tumours. The results were compared with the literature and graded according to the level of evidence. Results: Adjuvant treatments benefit patients with a high risk of recurrence after surgery, and the agents used are pembrolizumab and sunitinib, with a preference for pembrolizumab. Neoadjuvant treatment is exceptional, even in initially unresectable cases. First-line treatment is mainly based on tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs); the choice of treatment is based on the International Metastatic Database Consortium (IMCD) risk score. Patients at favourable risk receive ICIs in combination with TKIs. Patients classified as intermediate or poor risk receive ICIs, without preference for ICI + ICIs or ICI + TKIs. Data on nonclear cell renal cancer treatment are limited. Active surveillance has a place in treating favourable-risk patients. Either denosumab or zoledronic acid can be used for treating metastatic bone disease. Conclusion: Immunotherapy and targeted therapy are the standards of care for advanced disease. The utilization and sequencing of these therapeutic agents hinge upon individual risk scores and responses to previous treatments. This consensus reflects a commitment to informed decision-making, drawn from professional expertise and evidence in the medical literature. [ABSTRACT FROM AUTHOR]

Background: To evaluate the clinical efficacy and safety of immune checkpoint inhibitors in patients with advanced gastric cancer in the real world. Methods: The retrospective analysis was conducted on the clinical records of 402 patients with advanced gastric cancer who were admitted to the Nanjing Drum Tower Hospital between December 2017 and April 2022 and who had received immunotherapy. Observation target: drug use, treatment, adverse reaction type and grade, objective response rate (ORR), disease control rate (DCR), progression free survival (PFS), and overall survival (OS). Results: By retrospectively analyzing the data of patients with advanced gastric cancer treated with ICIs previously admitted to our medical center, we found some clinical characteristic factors associated with the occurrence of irAEs as well as the efficacy and prognosis: the presence or absence of hypertension, whether or not to receive targeted therapies can predict the occurrence of immune-related adverse events (irAEs), and the more the presence of irAEs, the better the prognosis. These can help clinicians in clinical drug selection. Conclusions: The results of this paper show that the occurrence of irAEs is associated with patients’ OS. irAEs occurrence can prolong patients’ OS. irAEs occurrence may serve as a surrogate marker for ICIs. [ABSTRACT FROM AUTHOR]

Gas signaling molecules, including carbon monoxide (CO), nitric oxide (NO), and hydrogen sulfide (H2S), have been shown to have cancer therapeutic potential, pointing to a new direction for cancer treatment. In recent years, a series of studies have confirmed that hydrogen (H2), a weakly reductive gas, also has therapeutic effects on various cancers and can mitigate oxidative stress caused by radiation and chemotherapy, reducing tissue damage and immunosuppression to improve prognosis. Meanwhile, H2 also has immunomodulatory effects, inhibiting T cell exhaustion and enhancing T cell anti-tumor function. It is worth noting that human intestinal flora can produce large amounts of H2 daily, which becomes a natural barrier to maintaining the body's resistance to diseases such as tumors. Although the potential anti-tumor mechanisms of H2 are still to be investigated, previous studies have shown that H2 can selectively scavenge highly toxic reactive oxygen species (ROS) and inhibit various ROS-dependent signaling pathways in cancer cells, thus inhibiting cancer cell proliferation and metastasis. The ROS scavenging ability of H2 may also be the underlying mechanism of its immunomodulatory function. In this paper, we review the significance of H2 produced by intestinal flora on the immune homeostasis of the body, the role of H2 in cancer therapy and the underlying mechanisms, and the specific application of H2 to provide new ideas for the comprehensive treatment of cancer patients. [ABSTRACT FROM AUTHOR]

Background/objective: Cervical cancer is the major cause of cancer-related mortalities in women globally. It constitutes one of the life-threatening conditions for women in developing countries. The popularization of cervical cancer screening and the improvement of treatment levels has caused the mortality rate of cervical cancer to decrease gradually, but pelvic floor dysfunction before and after cervical cancer treatment has become prominent and attracted more and more attention. Bibliometric analysis has been carried out in this research. The main goal of this research is to provide a comprehensive insight into the knowledge structure and global research hotspots about pelvic floor dysfunction in cervical cancer. Methods: Literature related to cervical cancer and pelvic floor dysfunction as of May 2023 was searched on the Web of Science Core Collection (WOSCC). The visualization and bibliometric analyses of the number and contents of publications were performed to analyze the temporal trends, spatial distribution, collaborative networks, influential references, keyword co-occurrence, and clustering. Results: There were 870 publications from 74 countries or regions, with the U.S. publications in a leading position. Since 2020, the number of publications has rapidly increased with the emphasis on the quality of life of cervical cancer patients. Although pelvic floor dysfunction in cervical cancer mainly occurs in developing countries, developed countries have made great contributions to this disease. However, in developing countries such as China and India, the quality of publications needs to be improved. In this field, the studies focused on the sexual dysfunction or urinary incontinence of cervical cancer patients, and the most cited papers discussed the effect of cervical cancer treatment on the sexual activities of females. The frontier keywords were represented by pelvic radiotherapy and risk factors. Conclusion: This study provides an objective and comprehensive analysis of the literature available on pelvic floor dysfunction in cervical cancer and identifies future trends and current hotspots. It can provide a valuable reference for researchers in this field. [ABSTRACT FROM AUTHOR]

Purpose: Patients with polycythemia vera (PV) show an elevated incidence of thromboembolic complications and decreased survival when compared to age-matched healthy individuals. Hypercellularity as indicated by elevated hematocrit, pathophysiological changes induced by the JAK2 driver mutation and cardiovascular risk factors contribute to the increased incidence of thromboembolic events. Higher age and a history of thromboembolic events define a high-risk population of PV patients. Depending on the individual risk profile, phlebotomy or pharmacologic cytoreduction is recommended in combination with low-dose acetylsalicylic acid. Stringent cytoreduction is required for effective risk reduction. However, in recent reports, the rate of thromboembolic complications in PV patients under cytoreductive therapy appears still elevated compared to healthy individuals. This study reports on a chart review to assess for cytoreductive therapy of 1440 PV patients in real life. Methods: Forty-two eligible hematologists/oncologists in private practice treating patients with MPN were recruited to participate in a paper–pencil-based survey conducted between January 2019 and March 2020 in Germany. Physicians were asked to report primary documented data obtained from patient charts. Descriptive analyses were conducted to assess for patient characteristics, treatment modalities, risk factors and thromboembolic complications. Results: Data were collected from the patient charts of 1440 individuals diagnosed with PV. The patient population was older than those reported in multicenter trials with a median age of 72.2 years at the time of reporting and 63.5 years at diagnosis. Age was the main factor accounting for high-risk status with 84.7% of patients being above the age of 60 followed by thromboembolic complications reported in 21.3% of patients. The use of pharmacologic cytoreduction was highly variable between participating centers with an average of 60.7% and a range of 10.1–100%. Hydroxyurea was the most frequently used drug followed by ruxolitinib, while interferons were reported for a minority of patients. For 35.4% of patients a persistent need for phlebotomy in addition to cytoreductive treatment was reported. Although presence of high-risk criteria and insufficient disease control were reported as main triggers to initiate pharmacologic cytoreduction, 28.1% had elevated hematocrit values (> 45%) and 38.6% showed persistence of elevated leukocyte count (> 109/l) while on cytoreductive treatment. In contrast, physician-reported symptom burden was lower than published in clinical trials and patient-reported outcomes. The rate of patients experiencing thromboembolic complications was 32.2% at any time and 14.3% after diagnosis with most patients receiving acetylsalicylic acid and 10.8% remaining on oral anticoagulants or heparin. Conclusions: Cytoreductive treatment of high-risk PV in real life is highly variable regarding indication for cytoreduction and definition of therapy resistance. This study highlights the need for (i) improved risk stratification for thromboembolic events, (ii) consequent indication of pharmacologic cytoreduction in high-risk PV and (iii) attention to signs of therapy resistance that can trigger an earlier and stringent switch to second line agents. [ABSTRACT FROM AUTHOR]

Purpose: Merkel cell carcinoma (MCC) is a rare, aggressive neuroendocrine carcinoma arising from the skin. We aimed to review and deal with some of the most relevant controversial topics on the correct use of immunotherapy for the treatment of MCC.Methods: The primary search was carried out via PubMed, EMBASE, and the Cochrane Library (until 31st May, 2018), while other articles and guidelines were retrieved from related papers or those referenced in these papers. Additionally, we performed an extensive search on ClinicalTrials.gov to gather information on the ongoing clinical trials related to this specific topic.Results: We performed an up-to-date critical review taking into account the results of both retrospective and prospective published studies evaluating these issues: Are there any predictive criteria of response to immunotherapy? What is the correct place of immunotherapy in the treatment algorithm of MCC? What is the best choice after immunotherapy failure? What to do with patients for whom immunotherapy is not been feasible or contraindicated? How long should immunotherapy be prolonged, and what follow-up should be offered after complete response?Conclusion: The therapeutic landscape of MCC is rapidly evolving: many open issues will probably be resolved, and many other questions are likely to arise in the next few years. The results of ongoing prospective clinical trials and of several other studies on these issues are eagerly awaited. [ABSTRACT FROM AUTHOR]

Introduction: One of the most feared side effects of radiotherapy (RT) in the setting of breast cancer (BC) patients is cardiac toxicity. This side effect can jeopardize the quality of life (QoL) of long-term survivors. The impact of modern techniques of RT such as deep inspiration breath hold (DIBH) have dramatically changed this setting. We report and discuss the results of the literature overview of this paper. Materials and methods: Literature references were obtained with a PubMed query, hand searching, and clinicaltrials.gov. Results: We reported and discussed the toxicity of RT and the improvements due to the modern techniques in the setting of BC patients. Conclusions: BC patients often have a long life expectancy, thus the RT should aim at limiting toxicities and at the same time maintaining the same high cure rates. Further studies are needed to evaluate the risk–benefit ratio to identify patients at higher risk and to tailor the treatment choices. [ABSTRACT FROM AUTHOR]

Purpose: Brain tumors are among the most lethal forms of cancer, so early diagnosis is crucial. As a result of machine learning algorithms, radiologists can now make accurate diagnoses of tumors without resorting to invasive procedures. There are, however, a number of obstacles to overcome. To begin, classifying brain tumors presents a significant difficulty in developing the most effective deep learning framework. Furthermore, physically dividing the brain tumor is a time-consuming and challenging process that requires the expertise of medical professionals. Methods: Here, we have discussed the use of a fuzzy 3D highlighting method for the segmentation of brain tumors and the selection of suspect tumor areas based on the geometric characteristics of MRI scans. After features were extracted from the brain tumor section, the images were classified using two machine learning methods: a support vector machine technique optimized with the grasshopper optimization algorithm (GOA-SVM), and a deep neural network technique based on features selected with the genetic algorithm (GA-DNN). This classifies brain tumors into benign and malignant. Implemented on the MATLAB platform, the proposed method is evaluated for effectiveness using performance metrics like sensitivity, accuracy, specificity, and Youden index. Results: From these results, it is clear that the proposed strategy is significantly superior to the alternatives. The average classification accuracy was determined to be 97.53%, 97.65%, for GA-DNN and GOA-SVM, respectively. Conclusion: These findings may be a quick and important step to detect the presence of lesions at the same time as cancerous tumors in neurology diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed at summarizing evidence about effects of psycho-behavioral interventions (PBIs) on immune responses among cancer patients and analyzing quality of published studies so as to inform future researches. Methods: Literature retrieval utilized both highly inclusive algorithms searching randomized controlled studies published in English and Chinese and manual searching of eligible studies from references of relevant review papers. Two researchers examined the articles selected separately and extracted the information using a pre-designed form for soliciting data about the trials (e.g., sample size, disease status, intervention, immune responses) and quality ratings of the studies. Both narrative descriptions and meta-analysis (via Review manager 5) were used synthesizing the effects of PBIs on immune responses among cancer patients and state of art of the researches in this area. Results: Seventy-six RCTs met inclusion criteria. PBIs implemented were divided into three major categories including psychological state adjustment, physical activity and dietary modification. Immune indicators measured included CD4+ cells, CD8+ cells, CD4/CDC8+ ratio, CD3+ cells, NK cell activity, etc. Effects of PBIs on immune responses documented in individual papers were mixed and pooled analysis of CD4+ cells, CD4+/CD8+ ratio, CD3+ cells, NKCA, IgG, IgM and IL-2 showed modest effects. However, there were huge discrepancies in intervention effects between studies published in English and Chinese and the results should be interpreted with caution. Besides, most studies suffer from some quality flaws concerning blinding, randomization procedures, compliance, attrition and intention-to-treat analyses, etc. Conclusions: Although there are considerable evidences of PBI effects on some immune indicators, the effect sizes are modest and it is still premature to conclude whether PBIs have effects on immune functions among cancer patients. There is a clear need for much more rigorous efforts in this area and future researches should pay particular attention to intervention dose and focus, sample size and comparable immune measures. [ABSTRACT FROM AUTHOR]

Purpose: Skin cancer is one of the ten most common cancer types in the world. Early diagnosis and treatment can effectively reduce the mortality of patients. Therefore, it is of great significance to develop an intelligent diagnosis system for skin cancer. According to the survey, at present, most intelligent diagnosis systems of skin cancer only use skin image data, but the multi-modal cross-fusion analysis using image data and patient clinical data is limited. Therefore, to further explore the complementary relationship between image data and patient clinical data, we propose multimode data fusion diagnosis network (MDFNet), a framework for skin cancer based on data fusion strategy. Methods: MDFNet establishes an effective mapping among heterogeneous data features, effectively fuses clinical skin images and patient clinical data, and effectively solves the problems of feature paucity and insufficient feature richness that only use single-mode data. Results: The experimental results present that our proposed smart skin cancer diagnosis model has an accuracy of 80.42%, which is an improvement of about 9% compared with the model accuracy using only medical images, thus effectively confirming the unique fusion advantages exhibited by MDFNet. Conclusions: This illustrates that MDFNet can not only be applied as an effective auxiliary diagnostic tool for skin cancer diagnosis, help physicians improve clinical decision-making ability and effectively improve the efficiency of clinical medicine diagnosis, but also its proposed data fusion method fully exerts the advantage of information convergence and has a certain reference value for the intelligent diagnosis of numerous clinical diseases. [ABSTRACT FROM AUTHOR]

Purpose: Robotic surgical system has been gradually applied in thyroid neoplasms as a novel treatment for years, with presenting some superiorities as well as limitations. To compare the effectiveness and safety of robotic surgery with open surgery for the patients with thyroid neoplasms, this review was conducted Methods: We performed electronic search in CENTRAL, MEDLINE, EMBASE, CNKI, CBM, Opengray, and Sciencepaper Online databases and manual search in specific online databases and according to the reference list of relevant papers to get all the studies that compared the effectiveness and safety of robotic surgery with that of open surgery for patients with thyroid neoplasms. Last update was conducted in March 2020. Randomized-controlled trials, case–control studies, cohort studies, and cross-sectional surveys were all included. Results: In this review, 59 studies were included: two RCTs, 15 NRSs, 40 cohort studies, and two cross-sectional studies. Robotic surgery was found to be associated with longer operative duration, less retrieved lymph nodes, higher postoperative thyroglobulin before radioactive iodine ablation, similar complication incidence but less blood loss, better functional recovery, and higher cosmetic satisfaction compared to open surgery. Conclusions: Robotic surgery is a safe and feasible approach with remarkable superiority in reducing intraoperative damage and improving patients' quality of life compared to open surgery for thyroid neoplasms. Meanwhile, this procedure is also associated with long operative duration, insufficient removal of neck lymph nodes, which need to be given careful consideration. [ABSTRACT FROM AUTHOR]

Purpose: Purinergic P2X7 receptor (P2X7R) is a gated ion channel for which adenosine triphosphate (ATP) is a ligand. Activated P2X7R is widely expressed in a variety of immune cells and tissues and is involved in a variety of physiological and pathological processes. Studies have confirmed that P2X7R is involved in the regulation of tumor cell growth, stimulating cell proliferation or inducing apoptosis. Recent studies have found that P2X7R is abnormally expressed in lung cancer and is closely related to the carcinogenesis and development of lung cancer. In this paper, we comprehensively describe the structure, function, and genetic polymorphisms of P2X7R. In particular, the role and therapeutic potential of P2X7R in lung cancer are discussed to provide new targets and new strategies for the treatment and prognosis of clinical lung cancer. Methods: The relevant literature on P2X7R and lung cancer from PubMed databases is reviewed in this article. Results: P2X7R regulates the function of lung cancer cells by activating multiple intracellular signaling pathways (such as the JNK, Rho, HMGB1 and EMT pathways), thereby affecting cell survival, growth, invasion, and metastasis and patient prognosis. Targeting P2X7R with inhibitors effectively suppresses the growth and metastasis of lung cancer cells. Conclusion: In summary, P2X7R is expected to become a potential target for the treatment of lung cancer, and more clinical research is needed in the future to explore the effectiveness of P2X7R antagonists as treatments. [ABSTRACT FROM AUTHOR]