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PURPOSE: Cancer is a leading cause of global childhood mortality, affecting 400,000 children annually. While treatable with modern therapies, children living in low- and middle-income countries (LMICs) have limited access to care and lower survival rates. Hospital-based cancer registries (HBCRs) collect detailed patient information to critically evaluate and evolve care. The St. Jude Global Childhood Cancer Analytics Resource and Epidemiological Surveillance System (SJCARES) is a cloud-based HBCR network facilitating quality data collection of pediatric cancer. Wide variation in the success of implementation has warranted further research into the implementation approach, to create a sustainable and adaptable HBCR in LMICs. METHODS: Seven of 89 sites using the SJCARES registry were selected, stratified by global region and stage of implementation. Semi-structured interviews were conducted with key groups (clinicians, administrators, data clerks) using an interview guide developed from the Consolidation Framework for Implementation Research (CFIR). Interviews were conducted via a video-telephone software program and transcribed by a transcription service. Transcripts were thematically coded using rapid qualitative analysis. RESULTS: A total of 18 participants (11 clinicians, 4 administrators, 3 data clerks) were interviewed. Several barrier themes were identified, including: difficulty integrating the registry into existing workflow; lack of resources; lack of government or administrative support; and damaged, misplaced, or illegible medical records. Facilitator themes were identified, including: internal support for the registry; clear and extensive training; and dedicated support staff. CONCLUSION: Interviewed participants identified key barriers and facilitators to the implementation of the SJCARES registry across multiple phases. We plan to use these results to develop targeted implementation strategies including a readiness assessment tool to help guide more successful implementation of the SJCARES registry and other HBCRs in LMICs.

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This literature review is to present a new direction in developing better treatment or preventive measures. The larger the body of an organism, the more numerous the cells, which theoretically lead to a higher risk of cancer. However, observational studies suggest the lack of correlation between body size and cancer risk, which is known as Peto's paradox. The corollary of Peto's paradox is that large organisms must be cancer-resistant. Further investigation of the anti-cancer mechanisms in each species could be potentially rewarding, and how the anti-cancer mechanisms found in wild animals can help influence and develop more effective cancer treatment in humans is the main focus of this literature review. Due to a lack of research and understanding of the exact molecular mechanisms of the researched species, only a few (Elephants and rodents) that have been extensively researched have made substantive contributions to human oncology. A new research direction is to investigate the positively selective genes that are related to cancer resistance and see if homologous genes are presented in humans. Despite the great obstacle of applying anti-cancer mechanisms to the human body from phylogenetically distant species, this research direction of gaining insights through investigating cancer-resisting evolutionary adaptations in wild animals has great potential in human oncology research.

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Chimeric antigen receptor (CAR) T-cell therapy is a relatively new and innovative immunotherapy for haemato-oncological diseases. In the UK, CAR T-cell therapy can be used to treat some patients with relapsed or refractory acute lymphoblastic leukaemia or diffuse large B-cell lymphoma. However, CAR T-cell therapy can have side effects that have implications for patients' physical and psychosocial well-being and may induce adverse reactions that can cause life-threatening acute toxicities. Nurses may have a significant role throughout the CAR T-cell therapy process, including in supporting patient decision-making, administering infusions, monitoring patients, identifying and managing adverse reactions, and providing follow-up care. This article provides an overview of CAR T-cell therapy and describes some of its potential side effects and adverse reactions. The authors also consider the role of the nurse and the implications for the nursing workforce in terms of meeting the needs of the increasing numbers of patients who may become eligible for this treatment as it is extended to other cancer types.

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Background: The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) that is developed and maintained by the Observational Health Data Sciences and Informatics (OHDSI) community supports large scale cancer research by enabling distributed network analysis. As the number of studies using the OMOP CDM for cancer research increases, there is a growing need for an overview of the scope of cancer research that relies on the OMOP CDM ecosystem. Objectives: In this study, we present a comprehensive review of the adoption of the OMOP CDM for cancer research and offer some insights on opportunities in leveraging the OMOP CDM ecosystem for advancing cancer research. Materials and Methods: Published literature databases were searched to retrieve OMOP CDM and cancer-related English language articles published between January 2010 and December 2023. A charting form was developed for two main themes, i.e., clinically focused data analysis studies and infrastructure development studies in the cancer domain. Results: In total, 50 unique articles were included, with 30 for the data analysis theme and 23 for the infrastructure theme, with 3 articles belonging to both themes. The topics covered by the existing body of research was depicted. Conclusion: Through depicting the status quo of research efforts to improve or leverage the potential of the OMOP CDM ecosystem for advancing cancer research, we identify challenges and opportunities surrounding data analysis and infrastructure including data quality, advanced analytics methodology adoption, in-depth phenotypic data inclusion through NLP, and multisite evaluation.

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BACKGROUND: Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient. OBJECTIVE: The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care. METHODS: To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research. RESULTS: This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template. CONCLUSIONS: The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review's objectives. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58705.

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The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.

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OBJECTIVE: Rare but aggressive cancer types like non-pancreatic periampullary cancers pose unique challenges for cancer research due to their low incidence rates and lack of consensus on optimal treatment strategies, therefore necessitating a collaborative approach. The International Study Group on non-pancreatic peri-Ampullary CAncer (ISGACA) aimed to build a collaborative initiative to pool expertise, funding opportunities, and data from over 60 medical centers, in order to improve outcomes for underrepresented patients with rare cancers. METHODS: The ISGACA approach predefined a stepwise approach including a research scope, establishing a dedicated steering committee, creating a recognizable brand, identifying research gaps, following a well-defined timeline, ensuring robust data collection, addressing legal and ethical considerations, securing financial resources, investing in research ethics training and statistical expertise, raising awareness, creating uniformity, and initiating prospective studies. RESULTS: Overall, 60 medical centers joined the ISGACA consortium (41 in Europe, 15 in North-America, three in Asia, one in Australia). The database includes 4309 patients. Nine publications and several ongoing studies which in turn allowed for a successful application of research grants. Subsequently, an international consensus meeting established uniform definitions and classifications, and one prospective multicenter international clinical trial has been initiated. CONCLUSION: By sharing knowledge, expertise, and clinical data, the ISGACA approach has not only gathered sufficient evidence to secure grants and ethical approvals for prospective studies, but also demonstrates options for standardizing patient care and improving outcomes for patients with rare cancers. The ISGACA approach offers a detailed methodology for initiating research on rare cancers and could serve as a replicable model for future research initiatives.

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Resistin is one of the most important adipokines, and its role lies mainly in controlling insulin sensitivity and inflammation. However, over the last years, the study of resistin gained increased popularity since it was proved that there is a considerable relationship between high levels of resistin and obesity as well as obesity-induced diseases, including diabetes, cardiovascular disorders, and cancer. Regarding cancer risk, circulating resistin levels have been correlated with several types of cancer, including colorectal, breast, lung, endometrial, gastroesophageal, prostate, renal, and pancreatic cancer. Colorectal cancer is regarded as a multi-pathway disease. Several pathophysiological features seem to promote colorectal cancer (CRC) such as chronic inflammation, insulin resistance, and obesity. Even though the molecular mechanisms involved in CRC development remain rather vague, it is widely accepted that several biochemical factors promote CRC by releasing augmented pro-inflammatory cytokines, like IGF-I, insulin, sex-steroid hormones, and adipokines. A wide range of research studies has focused on evaluating the impact of circulating resistin levels on CRC risk and determining the efficacy of chemotherapy in CRC patients by measuring resistin levels. Moreover, significant outcomes have emerged regarding the association of specific single nucleotide polymorphisms (SNPs) in the resistin gene and CRC risk. The present study reviewed the role of circulating resistin levels in CRC development and shed light on specific resistin gene SNPs implicated in the disease's development. Finally, we analyzed the impact of resistin levels on the effectiveness of chemotherapy and further discussed whether resistin can be regarded as a valuable biomarker for CRC prognosis and treatment. Resistin is one of the most important adipokines, and its role lies mainly in controlling insulin sensitivity and inflammation. However, over the last years, the study of resistin gained increased popularity since it was proved that there is a considerable relationship between high levels of resistin and obesity as well as obesity-induced diseases, including diabetes, cardiovascular disorders, and cancer. This review discusses the aberrant expression of resistin and its receptors, its diverse downstream signaling, and its impact on tumor growth, metastasis, angiogenesis, and therapy resistance to support its clinical exploitation in biomarker and therapeutic development.

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Organoid technology has been developed rapidly in the past decade, which involves the exploration of the mechanism of development, regeneration and various diseases, and intersects among multiple disciplines. Thousands of literature on 3D-culture or organoids have been published in the research areas of cell biology tissue engineering, nanoscience, oncology and so on, resulting in it being challenging for researchers to timely summarize these studies. Bibliometric statistics is a helpful way to help researchers clarify the above issues efficiently and manage the whole landscape systematically. In our study, all original articles on organoids were included in the Web of Science database from January 2009 to May 2024, and related information was collected and analyzed using Excel software, "bibliometrix" packages of the R software, VOSviewer and CiteSpace. As results, a total of 6222 papers were included to classify the status quo of the organoids and predict future research areas. Our findings highlight a growing trend in publications related to organoids, with the United States and Netherlands leading in this field. The University of California System, Harvard University, Utrecht University and Utrecht University Medical Center have emerged as pivotal contributors and the key authors in the field include Clevers, H, Beekman, JM and Spence JR. Our results also revealed that the research hotspots and trends of organoids mainly focused on clinical treatment, drug screening, and the application of materials and technologies such as "hydrogel" and "microfluidic technology" in organoids. Next, we had an in-depth interpretation of the development process of organoid research area, including the emergence of technology, the translation from bench to bedsides, the profiles of the most widely studied types of organoids, the application of materials and technologies, and the emerging organoid-immune co-cultures trends. Furthermore, we also discussed the pitfalls, challenges and prospects of organoid technology. In conclusion, this study provides readers straightforward and convenient access to the organoid research field.

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BACKGROUND: Advancements in cancer treatment and survivorship rely on participation in research and access to health records. METHODS: This study explored preferences for data access and sharing in 14 workshops with 42 community members, most of whom were a cancer survivor or carer. Various scenarios for data access and sharing were presented and discussed, with participants' preferences summarized using descriptive statistics. Reasons underlying these preferences were identified through a thematic analysis of workshop transcripts. RESULTS: Most participants indicated a willingness for researchers to use their self-report data and current health records for a specific research project (86%). Many were also willing for their self-report data and current (62%) or all future (44%) health records to be shared with other researchers for use in other studies if made aware of this. Willingness to consent to data access and sharing data in cancer research was influenced by: (i) the potential for data sharing to advance medical discoveries and benefit people impacted by cancer in the future, (ii) transparency around researchers' credibility and their intentions for data sharing, (iii) level of ownership and control over data sharing, and (iv) protocols for privacy and confidentiality in data sharing. CONCLUSIONS: Based on these themes, we present practical strategies for optimizing data access and sharing in cancer research.

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BACKGROUND: Cancer is a major public health challenge globally. However, little is known about the evolution patterns of cancer research communities and the influencing factors of their research capacity and impact, which is affected not only by the social networks established through research collaboration but also by the knowledge networks in which the research projects are embedded. METHODS: The focus of this study was narrowed to a specific topic - 'synthetic lethality' - in cancer research. This field has seen vibrant growth and multidisciplinary collaboration in the past decade. Multi-level collaboration and knowledge networks were established and analysed on the basis of bibliometric data from 'synthetic lethality'-related cancer research papers. Negative binomial regression analysis was further applied to explore how node attributes within these networks, along with other potential factors, affected paper citations, which are widely accepted as proxies for assessing research capacity and impact. RESULTS: Our study revealed that the synthetic lethality-based cancer research field is characterized by a knowledge network with high integration, alongside a collaboration network exhibiting some clustering. We found significant correlations between certain factors and citation counts. Specifically, a leading status within the nation-level international collaboration network and industry involvement were both found to be significantly related to higher citations. In the individual-level collaboration networks, lead authors' degree centrality has an inverted U-shaped relationship with citations, while their structural holes exhibit a positive and significant effect. Within the knowledge network, however, only measures of structural holes have a positive and significant effect on the number of citations. CONCLUSIONS: To enhance cancer research capacity and impact, non-leading countries should take measures to enhance their international collaboration status. For early career researchers, increasing the number of collaborators seems to be more effective. University-industry cooperation should also be encouraged, enhancing the integration of human resources, technology, funding, research platforms and medical resources. Insights gained through this study also provide recommendations to researchers or administrators in designing future research directions from a knowledge network perspective. Focusing on unique issues especially interdisciplinary fields will improve output and influence their research work.

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Precision oncology promises individually tailored drugs and clinical care for patients with cancer: That is, "the right drug, for the right patient, at the right dose, and at the right time." Although stratification of the risk for treatment resistance and toxicity is key to precision oncology, there are multiple ways in which such stratification can be achieved, for example, genetic, functional pathway based, among others. Moving toward precision oncology is sorely needed in the case of acute lymphoblastic leukemia (ALL) wherein adult patients display survival rates ranging from 30% to 70%. The present study reports on the pathway activity signature of adult B-ALL, with an eye to precision oncology. Transcriptome profiles from three different expression datasets, comprising 346 patients who were adolescents or adults with B-ALL, were harnessed to determine the activity of signaling pathways commonly disrupted in B-ALL. Pathway activity analyses revealed that Ph-like ALL closely resembles Ph-positive ALL. Although this was the case at the average pathway activity level, the pathway activity patterns in B-ALL differ from genetic subtypes. Importantly, clustering analysis revealed that five distinct clusters exist in B-ALL patients based on pathway activity, with each cluster displaying a unique pattern of pathway activation. Identifying pathway-based subtypes thus appears to be crucial, considering the inherent heterogeneity among patients with the same genetic subtype. In conclusion, a pathway-based stratification of the B-ALL could potentially allow for simultaneously targeting highly active pathways within each ALL subtype, and thus might open up new avenues of innovation for personalized/precision medicine in this cancer that continues to have poor prognosis in adult patients compared with the children.

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Metaplastic breast carcinoma (MBC) represents a rare subtype of breast cancer, characterized by poor prognostic indicators that have been recently identified. Clinical and radiological presentations often mimic other breast cancer types, necessitating immunohistochemistry (IHC) for accurate diagnosis. In this study, we report a case involving a 31-year-old female presenting with a painless, fixed, non-inflammatory mass in the left breast, which was confirmed as MBC. Treatment encompassed lumpectomy, chemotherapy, radiotherapy, and subsequent hormonal therapy. Understanding this rarely reported yet aggressive form of breast cancer holds significant importance for clinicians, enabling them to promptly establish a diagnosis and implement effective management strategies.

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BACKGROUND: Selecting pembrolizumab monotherapy (MONO) or pembrolizumab plus platinum-based chemotherapy (COMB) for patients with nonsmall cell lung cancer (NSCLC) and high programmed death-ligand 1 (PD-L1) expression is an important issue in clinical practice. We previously conducted a retrospective multicenter observational study of patients with NSCLC and high PD-L1 expression who received MONO or COMB as a first-line treatment. Here, we report updated data and evaluate the long-term outcomes. METHODS: We performed a retrospective multicenter study of 298 patients with NSCLC and high PD-L1 expression who received MONO or COMB as first-line treatment between December 2018 and January 2020. We reviewed the medical records and assessed the clinical efficacy and toxicity using a prolonged data cutoff. RESULTS: In total, 164 (median age: 74 years) and 134 (median age: 68 years) patients received MONO and COMB, respectively; patients who received COMB were younger and had better performance statuses (0-1). At the prolonged data cutoff, the median follow-up was 20.2 (range: 0.1-41.4) months. The median progression-free survivals were 7.5 and 13.1 months, and overall survivals (OSs) were 17.2 and 33.7 months for MONO and COMB, respectively. Treatment discontinuation rates were 21.9% and 20.1% for the MONO and COMB, respectively. With prolonged follow-up, although COMB demonstrated an OS benefit and higher objective response rate than MONO, in the propensity score matching analysis COMB didn't demonstrate a significant benefit compared to the MONO. CONCLUSIONS: COMB may be effective as a first-line treatment for NSCLC with high PD-L1 expression in a selected subset of patients.

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For mammary carcinomas in pet rabbits, prognostic biomarkers are poorly defined, and treatment is limited to surgical excision. Additional treatment options are needed for rabbit patients for which surgery is not a suitable option. In human breast cancer, the immunosuppressive enzyme indoleamine 2,3-dioxygenase 1 (IDO1) represents a prognostic biomarker and possible therapeutic target. This retrospective immunohistochemical study examined IDO1 in 96 pet rabbit mammary carcinomas with known mitotic count, hormone receptor status, and percentage of stromal tumor infiltrating lymphocytes (TILs). Tumors were obtained from 96 pet rabbits with an average of 5.5 years. All rabbits with reported sex (n = 88) were female or female-spayed. Of the carcinomas, 94% expressed IDO1, and 86% had sparse TILs consistent with cold tumors. Statistically significant correlations existed between a higher percentage of IDO1-positive tumor cells, lower mitotic counts, and increased estrogen receptor expression. The threshold for significance was IDO1 staining in >10% of tumor cells. These results lead to the assumption that IDO1 expression contributes to tumorigenesis and may represent a prognostic biomarker and possible therapeutic target also in pet rabbit mammary carcinomas. They also support the value of rabbits for breast cancer research.

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Lung cancer (LC) is a highly invasive malignancy and the leading cause of cancer-related deaths, with non-small cell lung cancer (NSCLC) as its most prevalent histological subtype. Despite all breakthroughs achieved in drug development, the prognosis of NSCLC remains poor. The mitogen-activated protein kinase signaling cascade (MAPKC) is a complex network of interacting molecules that can drive oncogenesis, cancer progression, and drug resistance when dysregulated. Over the past decades, MAPKC components have been used to design MAPKC inhibitors (MAPKCIs), which have shown varying efficacy in treating NSCLC. Thus, recent studies support the potential clinical use of MAPKCIs, especially in combination with other therapeutic approaches. This article provides an overview of the MAPKC and its inhibitors in the clinical management of NSCLC. It addresses the gaps in the current literature on different combinations of selective inhibitors while suggesting two particular therapy approaches to be researched in NSCLC: parallel and aggregate targeting of the MAPKC. This work also provides suggestions that could serve as a potential guideline to aid future research in MAPKCIs to optimize clinical outcomes in NSCLC.

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Breast cancer, a complex disease with a significant prevalence to form metastases, necessitates novel therapeutic strategies to improve treatment outcomes. Here, we present the results of a comparative molecular study of primary breast tumours, their metastases, and the corresponding primary cell lines using Desorption Electrospray Ionisation (DESI) and Laser-Assisted Rapid Evaporative Ionisation Mass Spectrometry (LA-REIMS) imaging. Our results show that ambient ionisation mass spectrometry technology is suitable for rapid characterisation of samples, providing a lipid- and metabolite-rich spectrum within seconds. Our study demonstrates that the lipidomic fingerprint of the primary tumour is not significantly distinguishable from that of its metastasis, in parallel with the similarity observed between their respective primary cell lines. While significant differences were observed between tumours and the corresponding cell lines, distinct lipidomic signatures and several phospholipids such as PA(36:2), PE(36:1), and PE(P-38:4)/PE(O-38:5) for LA-REIMS imaging and PE(P-38:4)/PE(O-38:5), PS(36:1), and PI(38:4) for DESI-MSI were identified in both tumours and cells. We show that the tumours' characteristics can be found in the corresponding primary cell lines, offering a promising avenue for assessing tumour responsiveness to therapeutic interventions. A comparative analysis by DESI-MSI and LA-REIMS imaging revealed complementary information, demonstrating the utility of LA-REIMS in the molecular imaging of cancer.

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PURPOSE: High-quality communication is essential to patient-centered care. Existing communication models and research tends to focus on what is said verbally with little attention to nonverbal aspects of communication. In sensitive and emotionally intensive healthcare encounters, such as in cancer care, provider and patient nonverbal behavior may be particularly important for communicating with empathy. Therefore, the aim of this study was to develop a conceptual model of communication that accounts for nonverbal behavior. METHODS: We followed a systematic grounded theory design that involved semi-structured interviews with 23 providers, including nurse practitioners, physicians, surgeons, and physician's assistants. Using constant comparative analysis, we analyzed transcripts and developed a grounded theory model of communication accounting for nonverbal behavior. RESULTS: The major themes included building rapport, gauging how patients will take bad news, ensuring patients' understanding of their conditions, staying honest but hopeful, centering but guiding patient through cancer care, conveying empathy while managing heightened emotions, and ensuring patient understanding. Throughout the process, providers synthesize both verbal and nonverbal information and apply what they learn to future encounters. CONCLUSIONS: The results extend existing models of patient-centered communication and invite communication intervention and research that incorporates nonverbal behavior. The model contributes an understanding of the full process of communication in clinical encounters.

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The study titled "Transient receptor potential-related risk model predicts prognosis of hepatocellular carcinoma patients" is a significant contribution to hepatocellular carcinoma (HCC) research, highlighting the role of transient receptor potential (TRP) family genes in the disease's progression and prognosis. Utilizing data from The Cancer Genome Atlas database, it establishes a new risk assessment model, emphasizing the interaction of TRP genes with tumor proliferation pathways, key metabolic reactions like retinol metabolism, and the tumor immune microenvironment. Notably, the overexpression of the TRPC1 gene in HCC correlates with poorer patient survival outcomes, suggesting its potential as a prognostic biomarker and a target for personalized therapy, particularly in strategies combining immunotherapy and anti-TRP agents.

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Cancer, being one of the deadliest diseases, poses significant challenges despite the existence of traditional treatment approaches. This has led to a growing demand for innovative pharmaceutical agents that specifically target cancer cells for effective treatment. In recent years, the use of metal nanoparticles (NPs) as a promising alternative to conventional therapies has gained prominence in cancer research. Metal NPs exhibit unique properties that hold tremendous potential for various applications in cancer treatment. Studies have demonstrated that certain metals possess inherent or acquired anticancer capabilities through their surfaces. These properties make metal NPs an attractive focus for therapeutic development. In this review, we will investigate the applicability of several distinct classes of metal NPs for tumor targeting in cancer treatment. These classes may include gold, silver, iron oxide, and other metals with unique properties that can be exploited for therapeutic purposes. Additionally, we will provide a comprehensive summary of the risk factors associated with the therapeutic application of metal NPs. Understanding and addressing these factors will be crucial for successful clinical translation and to mitigate any potential challenges or failures in the translation of metal NP-based therapies. By exploring the therapeutic potential of metal NPs and identifying the associated risk factors, this review aims to contribute to the advancement of cancer treatment strategies. The anticipated outcome of this review is to provide valuable insights and pave the way for the advancement of effective and targeted therapies utilizing metal NPs specifically for cancer patients.