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Abstract AlphaFold model has reshaped biological research. However, vast unstructured data in the entire AlphaFold field requires further analysis to fully understand the current research landscape and guide future exploration. Thus, this scientometric analysis aimed to identify critical research clusters, track emerging trends, and highlight underexplored areas in this field by utilizing machine-learning-driven informatics methods. Quantitative statistical analysis reveals that the AlphaFold field is enjoying an astonishing development trend (Annual Growth Rate = 180.13%) and global collaboration (International Co-authorship = 33.33%). Unsupervised clustering algorithm, time series tracking, and global impact assessment point out that Cluster 3 (Artificial Intelligence-Powered Advancements in AlphaFold for Structural Biology) has the greatest influence (Average Citation = 48.36 ± 184.98). Additionally, regression curve and hotspot burst analysis highlight “structure prediction” (s = 12.40, R2 = 0.9480, p = 0.0051), “artificial intelligence” (s = 5.00, R2 = 0.8096, p = 0.0375), “drug discovery” (s = 1.90, R2 = 0.7987, p = 0.0409), and “molecular dynamics” (s = 2.40, R2 = 0.8000, p = 0.0405) as core hotspots driving the research frontier. More importantly, the Walktrap algorithm further reveals that “structure prediction, artificial intelligence, molecular dynamics” (Relevance Percentage[RP] = 100%, Development Percentage[DP] = 25.0%), “sars-cov-2, covid-19, vaccine design” (RP = 97.8%, DP = 37.5%), and “homology modeling, virtual screening, membrane protein” (RP = 89.9%, DP = 26.1%) are closely intertwined with the AlphaFold model but remain underexplored, which implies a broad exploration space. In conclusion, through the machine-learning-driven informatics methods, this scientometric analysis offers an objective and comprehensive overview of global AlphaFold research, identifying critical research clusters and hotspots while prospectively pointing out underexplored critical areas.

ABSTRACT Background Distinctive heterogeneity characterizes diffuse large B‐cell lymphoma (DLBCL), one of the most frequent types of non‐Hodgkin's lymphoma. Mitochondria have been demonstrated to be closely involved in tumorigenesis and progression, particularly in DLBCL. Objective The purposes of this study were to identify the prognostic mitochondria‐related genes (MRGs) in DLBCL, and to develop a risk model based on MRGs and machine learning algorithms. Methods Transcriptome profiles and clinical information were obtained from the Gene Expression Omnibus (GEO) database. The risk model was defined using Least Absolute Shrinkage and Selection Operator (Lasso) regression algorithm, and its prognostic value was further examined in independent datasets. Patients were stratified into two clusters based on the risk scores, additionally a nomogram was generated based on the risk score and clinical characteristics. Gene pathway level, microenvironment, expression of targeted therapy‐associated genes, response to immunotherapy, drug sensitivity, and somatic mutation status were compared between clusters. Results Eighteen prognostic MRGs (DNM1L, PUSL1, CHCHD4, COX7A1, CPT1A, CYP27A1, POLDIP2, PCK2, MRPL2, PDK3, PDK4, MARC2, ACSM3, COA7, THNSL1, ATAD3B, C15orf48, TOMM70A) were identified to construct the risk model. Remarkable discrepancies were observed between groups. The high‐risk group had shorter overall survival, less immune infiltration, lower CD20 and higher PD‐L1 expression than the low‐risk group. Distinct immune microenvironment, responses to immunotherapy and predictive drug IC50 values were found between groups. Conclusions We established a novel prognostic mitochondria‐related signature by machine learning algorithm, which also demonstrated outstanding predictive value in tumor microenvironment and responses to therapies.

Abstract Liquid-liquid phase separation (LLPS) is a complex and subtle phenomenon whose formation and regulation take essential roles in cancer initiation, growth, progression, invasion, and metastasis. This domain holds a wealth of underutilized unstructured data that needs further excavation for potentially valuable information. Therefore, we retrospectively analyzed the global scientific knowledge in the field over the last decade by using informatics methods (such as hierarchical clustering, regression statistics, hotspot burst, and Walktrap algorithm analysis). Over the past decade, this area enjoyed a favorable development trend (Annual Growth Rate: 34.98%) and global collaboration (International Co-authorship: 27.31%). Through unsupervised hierarchical clustering based on machine learning, the global research hotspots were divided into five dominant research clusters: Cluster 1 (Effects and Mechanisms of Phase Separation in Drug Delivery), Cluster 2 (Phase Separation in Gene Expression Regulation), Cluster 3 (Phase Separation in RNA-Protein Interaction), Cluster 4 (Reference Value of Phase Separation in Neurodegenerative Diseases for Cancer Research), and Cluster 5 (Roles and Mechanisms of Phase Separation). And further time-series analysis revealed that Cluster 5 is the emerging research cluster. In addition, results from the regression curve and hotspot burst analysis point in unison to super-enhancer (a=0.5515, R2=0.6586, p=0.0044) and stress granule (a=0.8000, R2=0.6000, p=0.0085) as the most potential star molecule in this field. More interestingly, the Random-Walk-Strategy-based Walktrap algorithm further revealed that “phase separation, cancer, transcription, super-enhancer, epigenetics”(Relevance Percentage[RP]=100%, Development Percentage[DP]=29.2%), “stress granule, immunotherapy, tumor microenvironment, RNA binding protein”(RP=79.2%, DP=33.3%) and “nanoparticle, apoptosis”(RP=70.8%, DP=25.0%) are closely associated with this field, but are still under-developed and worthy of further exploration. In conclusion, this study profiled the global scientific landscape, discovered a crucial emerging research cluster, identified several pivotal research molecules, and predicted several crucial but still under-developed directions that deserve further research, providing an important reference value for subsequent basic and clinical research of phase separation in cancer.

Abstract The rapidly increasing use of high-throughput screening had produced a plethora of expanding knowledge on the molecular basis of natural killer/T-cell lymphoma (NKTCL), which in turn has revolutionized the treatment. Specifically, the use of asparaginase-containing regimens has led to substantial improvement in survival outcomes in NKTCL patients. Novel treatment strategies that are currently under development include cell-surface-targeted antibodies, immune checkpoint inhibitors, Epstein-Barr virus targeted cytotoxic T lymphocyte, immunomodulatory agents, chimeric antigen receptor T cells, signaling pathway inhibitors and epigenetic targeted agents. In almost all cases, initial clinical studies of newly developed treatment are conducted in patients relapsed, and refractory NKTCL due to very limited treatment options. This review summarizes the results of these novel treatments for NKTCL and discusses their potential for likely use in NKTCL in a wider setting in the future.

Abstract Background High-intensity chemotherapy regimens are often used in adult T-cell lymphoblastic lymphoma (T-LBL) patients. Nevertheless, the response rate remains unsatisfactory due to emergence of chemoresistance. Growing evidence has shown that long non-coding RNAs (lncRNAs) are involved in tumor progression and chemoresistance. Herein, we investigated the potential role of lncRNAs in T-LBLs. Methods RNAseq was used to screen and identify candidate lncRNAs associated with T-LBL progression and chemoresistance. Luciferase reporter assay was used to examine the binding of miR-371b-5p to the 3’UTR of Smad2 and LEF1, and the binding of TCF-4/LEF1 to the promoter of LINC00183. Chromatin immunoprecipitation assay was undertaken to analyze the connection between LEF1 and the LINC00183 promoter region. RNA immunoprecipitation assays were used to explore the mechanism whereby LINC00183 regulated miR-371b-5p. MTT and flow cytometry assays were used to measure apoptosis of T-LBL cells. Results LINC00183 was upregulated in T-LBL progression and chemoresistant tissues in both the Sun Yat-sen University Cancer Center dataset and the First Affiliated Hospital of Anhui Medical University dataset. High expression of LINC00183 was correlated with poorer overall survival and progression-free survival of T-LBL patients compared to those with low expression of LINC00183. Furthermore, miR-371b-5p was negatively regulated by LINC00183. In vivo and in vitro assays showed that LINC00183-mediated T-LBL chemoresistance depended on miR-371b-5p expression. The direct binding of miR-371b-5p to Smad2 and LEF1 was verified by luciferase assays. It was shown that TCF4/LEF1 could bind to the LINC00183 promoter site and increase its transcript level. Downregulation of miR-371b-5p led to increased expression of Smad2/LEF1, and in turn increased LINC00183 expression. Additionally, phospho-Smad2 promotes nuclear translocation of β-catenin, LINC00183 downregulation decreased chemoresistance induced by β-catenin and TGF-β1 in T-LBL cells. Conclusion We unraveled a β-catenin-LINC00183-miR-371b-5p-Smad2/LEF1 feedback loop that promotes T-LBL progression and chemoresistance, indicating that LINC00183 may serve as a potential therapeutic target in T-LBLs.

BackgroundThere is a wealth of poorly utilized unstructured data on lymphoma metabolism, and scientometrics and visualization study could serve as a robust tool to address this issue. Hence, it was implemented.MethodsAfter strict quality control, numerous data regarding the lymphoma metabolism were mined, quantified, cleaned, fused, and visualized from documents (n = 2925) limited from 2013 to 2022 using R packages, VOSviewer, and GraphPad Prism.ResultsThe linear fitting analysis generated functions predicting the annual publication number (y = 31.685x - 63628, R² = 0.93614, Prediction in 2027: 598) and citation number (y = 1363.7x - 2746019, R² = 0.94956, Prediction in 2027: 18201). In the last decade, the most academically performing author, journal, country, and affiliation were Meignan Michel (n = 35), European Journal of Nuclear Medicine and Molecular Imaging (n = 1653), USA (n = 3114), and University of Pennsylvania (n = 86), respectively. The hierarchical clustering based on unsupervised learning further divided research signatures into five clusters, including the basic study cluster (Cluster 1, Total Link Strength [TLS] = 1670, Total Occurrence [TO] = 832) and clinical study cluster (Cluster 3, TLS = 3496, TO = 1328). The timeline distribution indicated that radiomics and artificial intelligence (Cluster 4, Average Publication Year = 2019.39 ± 0.21) is a relatively new research cluster, and more endeavors deserve. Research signature burst and linear regression analysis further confirmed the findings above and revealed additional important results, such as tumor microenvironment (a = 0.6848, R² = 0.5194, p = 0.019) and immunotherapy (a = 1.036, R² = 0.6687, p = 0.004). More interestingly, by performing a “Walktrap” algorithm, the community map indicated that the “apoptosis, metabolism, chemotherapy” (Centrality = 12, Density = 6), “lymphoma, pet/ct, prognosis” (Centrality = 11, Density = 1), and “genotoxicity, mutagenicity” (Centrality = 9, Density = 4) are crucial but still under-explored, illustrating the potentiality of these research signatures in the field of the lymphoma metabolism.ConclusionThis study comprehensively mines valuable information and offers significant predictions about lymphoma metabolism for its clinical and experimental practice.

Cancer is a consequence of uncontrolled cell proliferation that is associated with cell-cycle disruption. It is a multifactorial disease that depends on the modulation of numerous oncogenic signaling pathways and targets. Although a battle against cancer has been waged for centuries, this disease remains a major cause of death worldwide. Because of the development of resistance to current anticancer drugs, substantial effort has been focused on discovering more effective agents for tumor therapy. Natural products have powerful prospects as anticancer drugs. Brusatol, a component isolated from the plant Brucea javanica, has been demonstrated to efficiently combat a wide variety of tumors. Extensive studies have indicated that brusatol exhibits anticancer effects by arresting the cell cycle; promoting apoptosis; inducing autophagy; attenuating epithelial-mesenchymal transition; inhibiting migration, invasion and angiogenesis; and increasing chemosensitivity and radiosensitivity. These effects involve various oncogenic signaling pathways, including the MAPK, NF-κB, PI3K/AKT/mTOR, JAK/STAT and Keap1/Nrf2/ARE signaling pathways. This review describes the evidence suggesting that brusatol is a promising drug candidate for cancer therapeutics.

Background: Nrf2, an essential and fascinating transcription factor, enjoys a dual property in the occurrence and development of inflammation and cancer. For over two decades, numerous studies regarding Nrf2 in cancer have been reported, whereas there is still a lack of a scientometrics and visualization analysis of Nrf2 in cancer. Hence, a scientometric study regarding the oxidative stress modulator Nrf2 was implemented. Methods: After the quality screening, we defined 7168 relevant studies from 2000 to 2021. CiteSpace, VOSviewer, R software, and GraphPad Prism were used for the following scientometric study and visualization analysis, including field profiles, research hotspots, and future predictions. Results: The total number of publications and citations are 1058 and 54,690, respectively. After polynomial fitting curve analysis, two prediction functions of the annual publication number (y = 3.3909x2 - 13585x + 1 E+07) and citation number (185.45x2 - 743669x + 7 E+08) were generated. After scientometric analysis, we found that Biochemistry Molecular Biology correlates with Nrf2 in cancer highly, and Free Radical Biology and Medicine is a good choice for submitting Nrf2-related manuscripts. The current research hotspots of Nrf2 in cancer mainly focus on cancer therapy and its cellular and molecular mechanisms. “antioxidant response element (87.5)'', “gene expression (43.98)'', “antioxidant responsive element (21.14)'', “chemoprevention (20.05)'', “carcinogenesis (19.2)'', “cancer chemoprevention (18.45)'', “free radical (17.15)'', “response element (14.17)'', and “chemopreventive agent (14.04)'' are important for cancer therapy study. In addition, “glutathione-S-transferase (47)'', “keap1 (15.39)'', and “heme oxygenase 1 gene (24.35)'' are important for inflammation and cell fate study. More interestingly, by performing an “InfoMap” algorithm, the thematic map showed that the “immune response” is essential to oxidative stress modulator Nrf2 but not well developed, indicating it deserves further exploration. Conclusion: This study revealed field profiles, research hotspots, and future directions of oxidative stress modulator Nrf2 in inflammation and cancer research, and our findings will offer a vigorous roadmap for further studies in this field.

Background: Neoadjuvant and adjuvant immunotherapies for cancer have evolved through a series of remarkable and critical research advances; however, addressing their similarities and differences is imperative in clinical practice. Therefore, this study aimed to examine their similarities and differences from the perspective of informatics analysis. Methods: This cross-sectional study retrospectively analyzed extensive relevant studies published between 2014 and 2023 using stringent search criteria, excluding nonpeer-reviewed and non-English documents. The main outcome variables are publication volume, citation volume, connection strength, occurrence frequency, relevance percentage, and development percentage. Furthermore, an integrated comparative analysis was conducted using unsupervised hierarchical clustering, spatiotemporal analysis, regression statistics, and Walktrap algorithm analysis. Results: This analysis included 1373 relevant studies. Advancements in neoadjuvant and adjuvant immunotherapies have been promising over the last decade, with an annual growth rate of 25.18 vs. 6.52% and global collaboration (International Co-authorships) of 19.93 vs. 19.84%. Respectively, five dominant research clusters were identified through unsupervised hierarchical clustering based on machine learning, among which Cluster 4 (Balance of neoadjuvant immunotherapy efficacy and safety) and Cluster 2 (Adjuvant immunotherapy clinical trials) [Average Publication Year (APY): 2021.70 ± 0.70 vs. 2017.54 ± 4.59] are emerging research populations. Burst and regression curve analyses uncovered domain pivotal research signatures, including microsatellite instability (R2=0.7500, P=0.0025) and biomarkers (R2=0.6505, P=0.0086) in neoadjuvant scenarios, and the tumor microenvironment (R2=0.5571, P=0.0209) in adjuvant scenarios. The Walktrap algorithm further revealed that 'neoadjuvant immunotherapy, nonsmall cell lung cancer (NSCLC), immune checkpoint inhibitors, melanoma' and 'adjuvant immunotherapy, melanoma, hepatocellular carcinoma, dendritic cells' (Relevance Percentage: 100 vs. 100%, Development Percentage: 37.5 vs. 17.1%) are extremely relevant to this field but remain underdeveloped, highlighting the need for further investigation. Conclusion: This study identified pivotal research signatures and provided substantial predictions for neoadjuvant and adjuvant cancer immunotherapies. In addition, comprehensive quantitative comparisons revealed a notable shift in focus within this field, with neoadjuvant immunotherapy taking precedence over adjuvant immunotherapy after 2020; such a qualitative finding facilitate proper decision-making for subsequent research and mitigate the wastage of healthcare resources. [ABSTRACT FROM AUTHOR]