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.

Research on neoadjuvant immunotherapy (NAI) is increasingly focusing on immunotherapy-related adverse events (AEs). However, many unknowns remain in this field. Hence, through the machine learning (ML)-driven informatics analysis, this study aimed to profile the global decade-long scientific landscape of AEs of NAI and further reveal its critical issues and directions that deserve deeper exploration. During the past decade, the amount of research in the field of NAI safety has displayed a positive trend (annual growth rate: 30.2%), and it has achieved good global collaboration (international coauthorship: 17.43%). Using an unsupervised clustering algorithm, we identified six dominant research clusters, among which Cluster 1 (standardizing response assessment criteria for NAI to minimize its adverse reactions; average citation=34.86±95.48) had the highest impact and Cluster 6 (efficacy and safety of multiple therapy patterns combination) was an emerging research cluster (temporal central tendency=2022.43, research effort dispersion=0.52), with “irAEs” (s=0.4242 (95% CI: 0.01142 to 0.8371), R²=0.4125, p=0.0453), “ICIs” (immune checkpoint inhibitors) (s=1.127 (95% CI: 0.5403 to 1.714), R²=0.7103, p=0.0022), and “efficacy and safety” (s=0.5455 (95% CI: 0.1145 to 0.9764), R²=0.5157, p=0.0193) showing significant overall growth. More importantly, further hotspot burst analysis indicated “ICI” and “efficacy and safety” as the emerging research focuses, demonstrating that scholars in the field are increasingly aware of the importance of balancing NAI efficacy and safety. In conclusion, this study presents ML-derived evidence that outlines the safety challenges of NAI and highlights the importance of balancing its efficacy and safety for its application in patients with perioperative cancer.

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.

As a member of PHB (prohibitin1) family, PHB plays important roles in many cancers, but its property in bladder carcinoma aggressiveness is unknown. This research was to explore the function and potential mechanism of PHB in bladder carcinoma in vivo and in vitro. The invasive abilities of cancer cell were determined by transwell and wound-healing assays. The function of PHB was confirmed by gene knockdown and overexpression methods. Further in vivo confirmation was performed in a nude mouse model with lung metastasis. The relationship of PHB and β-catenin was confirmed by immunoprecipitation and immunofluorescence staining assays. The protein expression of epithelial-mescenchymal transition (EMT) and Wnt/β-catenin signaling pathway was tested by immunofluorescence staining and western blotting assay. The depletion of PHB prevented bladder cancer cell invasiveness and inhibited EMT. Contrarily，the abilities of bladder carcinoma cells migration and invasion in vitro as well as metastasis in vivo were enhanced when the PHB overexpressed unnormally. Importantly, the β-catenin was identified to be bound by PHB and β-catenin knockdown reduced the cancer cell migration, invasion and EMT in PHB overexpressing cells. In addition, PHB stabilized β-catenin by inhibiting its ubiqutin-mediated degradation thus leading to increased Wnt/β-catenin signaling. These observations indicate that PHB could promote bladder cancer aggressiveness by binding with β-catenin to prevent the degradation of β-catenin and the localized invasive bladder cancer patients with PHB overexpression should take more aggressive postsurgical adjuvant anticancer therapies.

Colorectal carcinoma (CRC) is one of the major causes of cancer-related incidence and deaths. Here, we identified a novel antitumor peptide, P6, with a molecular weight of 2794.8 Da from a marine Chinese medicine, Arca inflata Reeve. The full amino acid sequence and secondary structure of P6 were determined by tandem mass de novo sequencing and circular dichroism spectroscopy, respectively. P6 markedly inhibited cell proliferation and colony formation, and induced apoptosis in CRC cells. Mechanistically, transcriptomics analysis and a serial functional evaluation showed that P6 induced colon cancer cell apoptosis through the activation of the p38-MAPK signaling pathway. Moreover, it was demonstrated that P6 exhibited antitumor effects in a tumor xenograft model, and induced cell cycle arrest in CRC cells in a concentration-dependent mode. These findings provide the first line of indication that P6 could be a potential therapeutic agent for CRC treatment.