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.

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.

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.

Hemostatic alterations occur during the development of cancer. Plasma D-dimer is a hypercoagulability and fibrinolytic system marker that is increased in patients with various solid tumours. The aim of this study was to evaluate the hemostatic status of nasopharyngeal carcinoma (NPC) patients by assessing plasma D-dimer levels to investigate its value as a prognostic marker. We retrospectively analysed 717 patients with nasopharyngeal carcinoma, and we applied Cox regression and log-rank tests to assess the association of D-dimer levels with disease-free survival (DFS), distant metastasis-free survival (DMFS), and overall survival (OS). D-dimer levels were measured using a quantitative D-dimer latex agglutination assay. Using the 3rd quartile values (0.8 μg/L) as the optimal cut-offs, we found that patients with high D-dimer levels have a shorter 3-year DFS, (79%, 95%CI (73.1–84.9)) vs. (69%, 95%CI (59.2–78.8)), DMFS (87%, 95%CI (83.1–90.9)) vs. (77%, 95%CI (69.2–84.8)), and overall survival (82%, 95%CI (76.1–87.9)) vs. (76%, 95%CI (66.2–85.8)). Multivariate analysis revealed that pre-treatment D-dimer levels and EBV DNA were significant independent factors for DFS, DMFS, and OS in NPC patients. Subgroup analyses indicated that the plasma D-dimer levels could effectively stratify patient prognosis for early cancer, advanced stage cancer, and patients with EBV DNA ≥4000 copies/ml. High D-dimer levels were associated with poor disease-free survival, distant metastasis-free survival, overall survival, and increased risk of mortality in NPC patients. Prospective trials are required to assess the prognostic value of D-dimer levels.

Background: PIN2/TRF1-interacting telomerase inhibitor1 (PinX1) was recently suggested as a putative tumor suppressor in several types of human cancer, based on its binding to and inhibition of telomerase. Moreover, loss of PinX1 has been detected in many human malignancies. However, the possible involvement of PinX1 and its clinical/prognostic significance in urothelial carcinoma of the bladder (UCB) are unclear. Methods: The PinX1 expression profile was examined by quantitative real-time polymerase chain reaction (qRT-PCR), western blotting, and immunohistochemistry (IHC) in UCB tissues and adjacent normal urothelial bladder epithelial tissues. PinX1 was overexpressed and silenced in UCB cell lines to determine its role in tumorigenesis, development of UCB, and the possible mechanism. Results: PinX1 expression in UCB was significantly down-regulated at both mRNA and protein level as compared with that in normal urothelial bladder epithelial tissues. PinX1 levels were inversely correlated with tumor multiplicity, advanced N classification, high proliferation index (Ki-67), and poor survival (P < 0.05). Moreover, overexpression of PinX1 in UCB cells significantly inhibited cell proliferation in vitro and in vivo, whereas silencing PinX1 dramatically enhanced cell proliferation. Overexpression of PinX1 resulted in G1/S phase arrest and cell growth/proliferation inhibition, while silencing PinX1 led to acceleration of G1/S transition, and cell growth/proliferation promotion by inhibiting/enhancing telomerase activity and via the p16/cyclin D1 pathway. Conclusions: These findings suggest that down-regulation of PinX1 play an important role in the tumorigenesis and development of UCB and that the expression of PinX1 as detected by IHC is an independent molecular marker in patients with UCB.