Comparative investigation of neoadjuvant immunotherapy versus adjuvant immunotherapy in perioperative patients with cancer: a global-scale, cross-sectional, large-sample informatics study.

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 non-peer-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 1,373 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, non-small 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.

Global research hotspots, development trends and prospect discoveries of phase separation in cancer: a decade-long informatics investigation.

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.

An assessment system for clinical and biological interpretability in ulcerative colitis.

Ulcerative colitis (UC) is a serious inflammatory bowel disease (IBD) with high morbidity and mortality worldwide. As the traditional diagnostic techniques have various limitations in the practice and diagnosis of early ulcerative colitis, it is necessary to develop new diagnostic models from molecular biology to supplement the existing methods. In this study, we developed a machine learning-based synthesis to construct an artificial intelligence diagnostic model for ulcerative colitis, and the correctness of the model is verified using an external independent dataset. According to the significantly expressed genes related to the occurrence of UC in the model, an unsupervised quantitative ulcerative colitis related score (UCRScore) based on principal coordinate analysis was established. The UCRScore is not only highly generalizable across UC bulk cohorts at different stages, but also highly generalizable across single-cell datasets, with the same effect in terms of cell numbers, activation pathways and mechanisms. As an important role of screening genes in disease occurrence, based on connectivity map analysis, 5 potential targeting molecular compounds were identified, which can be used as an additional supplement to the therapeutic of UC. Overall, this study provides a potential tool for differential diagnosis and assessment of bio-pathological changes in UC at the macroscopic level, providing an opportunity to optimize the diagnosis and treatment of UC.

Comprehensive scientometrics and visualization study profiles lymphoma metabolism and identifies its significant research signatures.

Background: There 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. Methods: After 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. Results: The 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. Conclusion: This study comprehensively mines valuable information and offers significant predictions about lymphoma metabolism for its clinical and experimental practice.

Novel target and treatment agents for natural killer/T-cell lymphoma.

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.

A scientometrics and visualization analysis of oxidative stress modulator Nrf2 in cancer profiles its characteristics and reveals its association with immune response.

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.

PHB promotes bladder cancer cell epithelial-mesenchymal transition via the Wnt/ß-catenin signaling pathway.

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 ß-catenin was confirmed by immunoprecipitation and immunofluorescence staining assays. The protein expression of epithelial-mescenchymal transition (EMT) and Wnt/ß-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 ß-catenin was identified to be bound by PHB and ß-catenin knockdown reduced the cancer cell migration, invasion and EMT in PHB overexpressing cells. In addition, PHB stabilized ß-catenin by inhibiting its ubiqutin-mediated degradation thus leading to increased Wnt/ß-catenin signaling. These observations indicate that PHB could promote bladder cancer aggressiveness by binding with ß-catenin to prevent the degradation of ß-catenin and the localized invasive bladder cancer patients with PHB overexpression should take more aggressive postsurgical adjuvant anticancer therapies.

The ß-catenin-LINC00183-miR-371b-5p-Smad2/LEF1 axis promotes adult T-cell lymphoblastic lymphoma progression and chemoresistance.

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 ß-catenin, LINC00183 downregulation decreased chemoresistance induced by ß-catenin and TGF-ß1 in T-LBL cells. CONCLUSION: We unraveled a ß-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.