This study presents an extensive bibliometric analysis of cisplatin resistance (CR) in breast cancer (BC) from 2010 to 2024, elucidating global research trends, collaboration networks, and prospective research directions. Particular attention is given to novel therapeutic strategies, such as multi-target drug design and biomarker-guided treatments, aimed at overcoming challenges associated with drug resistance. This study utilizes the PubMed database and employs a topic search strategy, integrating the R package “bibliometrix” to conduct an in-depth analysis of the number of published documents, patterns of collaboration, journal impact, author contributions, institutional outputs, national collaboration networks, as well as keyword co-occurrences and citation networks. China and the United States are the principal contributors to research in this domain, with the Islamic Azad University and the journal Cancers serving as the primary platforms for academic dissemination. Notable researchers in this field include Wang, B. and Chekhun, V. F. Furthermore, the study highlights three particularly significant publications. Research hotspots include CR, triple-negative BC, BRCA1, DNA repair, microRNA, prostate cancer, ceRNA, LINCRNA, and prognosis, while trending topics comprise CR, triple-negative BC, BRCA1, autophagy, cytotoxicity, and DNA damage. This study provides actionable insights into research trends and translational opportunities in BC CR, emphasizing the integration of microRNA regulation, autophagy mechanisms, and multi-target drug design in clinical applications. Collaborative efforts between leading countries and institutions are pivotal to advancing therapeutic strategies and improving patient outcomes.

The globalization of healthcare has fueled the rapid growth of cross-border medical tourism, particularly in regions with significant disparities in healthcare infrastructure. In South Asia, cancer patients increasingly seek treatment abroad due to a lack of specialized oncology services, affordability constraints, and regulatory barriers. This research critically examines the intersection of cross-border healthcare policies and cancer care, identifying key push and pull factors that influence medical travel in the region. The study employs a document analysis and case study approach to assess how national healthcare policies either facilitate or restrict access to international oncology treatment. Findings highlight the uneven distribution of cancer care resources, with countries, such as India, serving as medical hubs, while others, such as Bangladesh and Nepal face severe treatment shortages. Policy inconsistencies, visa restrictions, and healthcare agreements further complicate patient mobility. This research underscores the need for regional cooperation in standardizing medical regulations, improving patient safety measures, and streamlining healthcare policies to enhance access to timely and cost-effective cancer treatment. The study contributes to the discourse on regional health integration by proposing policy recommendations for harmonized cross-border healthcare frameworks within South Asia.

Interleukin-1 beta (IL-1β) is a central pro-inflammatory cytokine with critical roles in immune regulation, inflammation, and tumor biology. Synthesized as an inactive precursor and activated through inflammasome-mediated cleavage, IL-1β signals via the IL-1 receptor to orchestrate immune responses. While essential for host defense, sustained IL-1β activity in the tumor microenvironment promotes angiogenesis, metastasis, epithelial-mesenchymal transition, and immune suppression, thereby facilitating the progression of cancers such as breast, lung, pancreatic, and colorectal. Conversely, IL-1β can enhance anti-tumor immunity by driving dendritic cell maturation, T-cell priming, and pyroptosis, thereby contributing to beneficial immune surveillance in certain hematologic malignancies. This dual role presents both challenges and opportunities for therapeutic intervention. Clinical blockade of IL-1β with agents such as anakinra, canakinumab, and rilonacept has shown promise, notably in the CANTOS trial, where IL-1β inhibition was associated with a reduction in lung cancer incidence. However, outcomes in colorectal and pancreatic cancer remain variable. The potential for immune suppression, combined with the absence of predictive biomarkers, underscores the need for precision-based strategies. Emerging approaches, including serum and tissue IL-1β profiling, analysis of inflammasome components, liquid biopsy, spatial transcriptomics, and single-cell technologies, may enable context-specific modulation. This review synthesizes current understanding of IL-1β’s paradoxical functions in cancer, evaluates therapeutic strategies targeting its signaling axis, and highlights future directions for integrating IL-1β modulation into precision oncology.

Recent advances in non-genetic and evolutionary cancer genomics increasingly challenge the long-standing mutational and molecular interpretations of cancer. At the core of this shift lies a fundamental contradiction between the traditional multicellular interpretation of cancer and the emerging concept of a self-organizing unicellular system. Traditional models view cancer as an aberration within the multicellular framework. In contrast, evolutionary research reveals that cancer reflects an inversion to unicellularity, predominantly expressed in the stemgermline and its archaic unicellular genome. The origins of cancer’s stemgermline trace back to an ancestral lineage (Urgermline) whose genomic and regulatory features have been inherited by all modern stem cell lineages. Since the lifestyle of this Urgermline evolved during periods of historical hypoxia (approximately 1,600-800 million years ago [Mya]) and gradual atmospheric oxygen increase (800-550 Mya), parasitic unicellular systems, such as amoebae and cancer—particularly their stemgermlines—benefit considerably from the low oxygen gradients present in tissues and organs that offer specific key inducers, suppressors, regulators, and effectors of cancer cell systems. One could argue that cancer reconstructs an autonomous cell system, mirroring developments from the Meso- and Neoproterozoic, and the evolutionary transition toward early multicellularity. As a result, cancer follows fundamentally different rules than those governing stable multicellular systems. Accordingly, conventional multicellular concepts such as uncontrolled proliferation, genomic chaos, genomic instability, and loss of genomic integrity are inadequate when viewed through evolutionary biology. The present work compares the mutational-molecular perspective of cancer with a non-genetic, evolutionary framework.

Breast cancer (BC) is the most prevalent cancer among women. Despite improvements in the detection and treatment of BC, drug resistance is widespread, making it the leading cause of cancer mortality in women. Long non-coding RNAs (lncRNAs) have been shown to play essential roles in regulating drug resistance in pre-clinical models. However, their clinical relevance remains largely unexplored. This review addresses this gap by identifying and examining lncRNAs with potential predictive value as biomarkers for drug resistance in BC cancer patients. A systematic search (last updated February 7, 2024) was conducted across five databases (Cochrane Library, Embase, PubMed, Scopus, and Web of Science) for research articles in English, published after 2010, involving BC patients who underwent treatment. Following the selection and review process, 66 studies were short-listed, and 185 unique lncRNAs linked to drug resistance in BC patients were identified. Notably, only five lncRNAs (BCAR4, CCAT2, DSCAM-AS1, GAS5, and H19) were reported in at least two independent studies, indicating the scarcity of replicated evidence in clinical cohorts. Receiver operating characteristic curve analysis for these five lncRNAs confirmed that BCAR4, GAS5, and H19 expression levels have prognostic potential for predicting chemotherapy response. However, further validation is required before lncRNAs can be effectively utilized as prognostic markers in a clinical setting.

Acute myeloid leukemia (AML) predominantly affects the elderly, who often have a poor prognosis due to age-related comorbidities, adverse genetic features, and limited tolerance to standard therapies. This study aimed to evaluate real-world survival outcomes and prognostic factors in elderly patients with AML to improve clinical management. A retrospective analysis was conducted on 179 elderly AML patients across multiple centers over 6 years. Clinical features, bone marrow characteristics, vital status, and prognostic factors were analyzed. Kaplan-Meier was used to estimate overall survival, while univariate and multivariate regression analyzes identified prognostic factors. The median overall survival (mOS) of the cohort was 5.3 months. Patients in the chemotherapy group (n = 126) showed better mOS than those in the support therapy group (n = 53) (7.567 vs. 3 months; p<0.0001). Among chemotherapy patients, those treated with hypomethylating agents (HMAs) (n = 54) had better mOS compared to cytotoxic chemotherapy (n = 72) (10.17 vs. 4.1 months; p<0.0001). Within the HMA group, no significant difference in mOS was found between HMA monotherapy (n = 9) and HMA plus venetoclax (VEN) (n = 45) (9.117 vs. 10.17 months; p=0.3407). In patients eligible for intensive chemotherapy, the HMA group had a superior mOS than the cytotoxic chemotherapy group (9.4 vs. 3.933 months; p<0.0001). The top five mutations identified were NPM1 (29.59%), FT3-internal tandem duplication (ITD) (26.53%), DNMT3A (25.51%), IDH2 (20.41%), and CEBPA (14.29%), with DNMT3A-FLT3-ITD, NPM1-FLT3-ITD, and NPM1-DNMT3A showing significantly higher co-mutation frequencies than the other combinations. In addition, infection was the most frequent complication (60%). Elderly AML patients have poor mOS and a high burden of adverse genetic features. Chemotherapy, especially HMAs alone or combined with VEN, is associated with improved survival and better clinical outcomes compared to supportive care or intensive regimens. These findings provide real-world evidence to inform treatment strategies.

The microbiome has been increasingly recognized as a crucial factor in cancer development and treatment. To guide future research by identifying key trends and thematic directions in cancer microbiome studies, we conducted a bibliometric analysis of 6,454 publications indexed in the Web of Science Core Collection between 2009 and 2024. The United States and China led in publication output and international collaboration. Prominent keywords included “gut microbiome,” “colorectal cancer,” “immunotherapy,” “intratumoral microbiome,” and “metabolism.” Rapidly emerging research areas encompassed the causal relationship between the microbiome and cancer, the role of microbial metabolites, the impact of dietary interventions on the microbiome, and the interplay between the intratumor microbiome and the tumor microenvironment. Co-citation network analysis revealed widely used analytical tools including QIIME and DADA2 for marker-gene sequencing, LEfSe for identifying taxa with differential abundance, and SIAMCAT for investigating microbiome-host phenotype associations. Research on colorectal and breast cancers dominated the literature, highlighting a relative lack of studies on other malignancies such as brain tumors and sarcomas. These findings offer valuable insights into current research priorities and may guide future cancer microbiome research toward the development of microbiome-based early cancer diagnostics, personalized anticancer therapies, and non-invasive monitoring strategies.

To predict the epidermal growth factor receptor (EGFR) T790M status of patients with advanced non-small cell lung cancer (NSCLC) following the first-line first-/second-generation EGFR-tyrosine kinase inhibitor (EGFR-TKI) therapy, the related clinical features and chest computed tomography (CT) images of patients with advanced NSCLC in our hospital were retrospectively collected. All patients who met the criteria were randomly divided into training and validation cohorts. Then, a clinical model with the filtered clinical characteristics and a deep-learning model (DLM) were constructed. The area under the curve (AUC), specificity, sensitivity, accuracy, and decision curve analysis were used to evaluate model performance. In total, 66 patients met the inclusion criteria of the study (training cohort, n = 40; validation cohort, n = 26). EGFR19del and the use of gefitinib were significant (P < 0.05), and then, the clinical model was established using multivariate logistic regression analysis. The AUCs of the clinical model were 0.862 (95% confidence interval [CI], 0.570 - 0.966) and 0.755 (0.566 - 0.943) in the training and validation cohorts, respectively. The AUCs of the DLM from the chest CT image analysis were 0.839 (95% CI, 0.708 - 0.970) and 0.842 (0.680 - 1.000) in the training and validation cohorts, respectively. In the validation cohort, the DLM and clinical model exhibited an accuracy of 0.7308 and 0.5000, specificity of 0.6667 and 0.2000, positive probability values of 0.6429 and 0.4545, and negative probability values of 0.8333 and 0.7500, respectively. The DLM was developed using chest CT images to predict the EGFR T790M status following the first-line first- and second-generation EGFR-TKI treatment of advanced EGFR-positive NSCLC.