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

PAQR6 as a prognostic biomarker and potential therapeutic target in kidney renal clear cell carcinoma

Background

Progestin And AdipoQ Receptor Family Member VI (PAQR6) plays a significant role in the non-genomic effects of rapid steroid responses and is abnormally expressed in various tumors. However, its biological function in kidney renal clear cell carcinoma (KIRC) and its potential as a therapeutic target remain underexplored.

Methods

In this study, PAQR6 was identified as a critical oncogene by WGCNA algorithm and differential gene expression analysis using TCGA - KIRC and GSE15641 data. The differences in PAQR6 expression and its association with KIRC survival outcomes were investigated, and transcriptomic data were used to further elucidate PAQR6's biological functions. Moreover, XCELL and single - cell analysis assessed the correlation between PAQR6 expression and immune infiltration. TIDE algorithm was used to assess how well various patient cohorts responded to immune checkpoint therapy. Finally, the role of PAQR6 in the development of KIRC was verified through EdU, scratch assays, and Transwell assays.

Results

Our findings suggest that elevated expression of PAQR6 is linked to a poor prognosis for KIRC patients. Functional enrichment analysis demonstrated that PAQR6 is primarily involved in angiogenesis and pluripotent stem cell differentiation, which are crucial in mediating the development of KIRC. Additionally, we established a ceRNA network that is directly related to overall prognosis, further supporting the role of PAQR6 as a prognostic biomarker for KIRC.

Conclusion

Using both computational and experimental methods, this study leads the charge in discovering and verifying PAQR6 as a prognostic biomarker and possible therapeutic target for KIRC. In the future, to determine its molecular mechanism in KIRC carcinogenesis, more in vivo research will be carried out.

Immune checkpoints in B-cell Lymphoma: Still an Unmet challenge from Basic research to clinical practice

In the last decade, advancements in immunotherapy knowledge have highlighted CTLA-4, PD-1, LAG-3, TIM-3, and TIGIT, decisive immune checkpoints exhibiting within the tumor microenvironment (TME), as fundamental objects for cancer immunotherapy. The widespread clinical use of immune checkpoint inhibitors (ICls), employing PD-1/PD-L1 or CTLA-4 antibodies to obstruct crucial checkpoint regulators, is noted in treating B-cell lymphoma patients. Nevertheless, the prolonged advantages of the currently employed treatments against CTLA-4, PD-1, and PD-L1 are uncommon among patients. Thus, recent focus has been progressively moved to additional immune checkpoints on T cells, like LAG-3, TIM-3, and TIGIT, which are now seen as reassuring targets for treatment and broadly acknowledged. There are several types of immunecheckpoint molecules expressed by T cells, and inhibitors targeting immune checkpoints can revive and amplify the immune response of T lymphocytes against tumors, a crucial aspect in lymphoma therapy. However, there is little knowledge about their regulation. Herein, we discuss the anti-tumor effects and functions of ICIs in controlling T-cell activity, as well as the progress in combined application with other immunotherapies.

Research trends, hotspots and future directions of tertiary lymphoid structures in cancer: a comprehensive informatics analysis and visualization study

Many studies have reported the presence of tertiary lymphoid structures (TLSs) in cancer, but the research progress of TLSs in cancer has not been systematically analyzed. Therefore, we analyzed the global scientific knowledge in the field using informatics methods. The results showed that TLSs in cancer have received increasing attention since the 21st century, with an annual publication growth rate of 27.86%. Unsupervised hierarchical clustering based on machine learning further categorized the research features into four clusters, with the cluster related to immunotherapy being considered an emerging cluster. TLSs and immunotherapy were identified as the top two hotspots with the highest occurrence frequency and total link strength. The Walktrap algorithm indicated that "TLSs, carcinoma, prognostic value" and "high endothelial venules, germinal-centers, node-like structures" are important to TLSs but remain underexplored, representing promising research directions. These findings suggest that cancer-related TLSs have brought new insights into antitumor immunity, and targeting TLSs has the potential to transform the landscape of antitumor immunotherapy.

Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation

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.

Molecular characterization and biomarker identification in paediatric B-cell acute lymphoblastic leukaemia

B-cell acute lymphoblastic leukaemia (B-ALL) is the most prevalent hematologic malignancy in children and a leading cause of mortality. Managing B-ALL remains challenging due to its heterogeneity and relapse risk. This study aimed to delineate the molecular features of paediatric B-ALL and explore the clinical utility of circulating tumour DNA (ctDNA). We analysed 146 patients with paediatric B-ALL who received systemic chemotherapy. The mutational landscape was profiled in bone marrow (BM) and plasma samples using next-generation sequencing. Minimal residual disease (MRD) testing on day 19 of induction therapy evaluated treatment efficacy. RNA sequencing identified gene fusions in 61% of patients, including 37 novel fusions. Specifically, the KMT2A-TRIM29 novel fusion was validated in a boy who responded well to initial therapy but relapsed after 1 year. Elevated mutation counts and maximum variant allele frequency in baseline BM were associated with significantly poorer chemotherapy response (p = 0.0012 and 0.028, respectively). MRD-negative patients exhibited upregulation of immune-related pathways (p < 0.01) and increased CD8+ T cell infiltration (p = 0.047). Baseline plasma ctDNA exhibited high mutational concordance with the paired BM samples and was significantly associated with chemotherapy efficacy. These findings suggest that ctDNA and BM profiling offer promising prognostic insights for paediatric B-ALL management.

Global research trends of nanomaterials application in periodontitis and peri-implantitis: A bibliometric analysis

Background

The application of nanomaterials (NMs) in the treatment of periodontitis and peri-implantitis has shown multifunctional benefits, such as antibacterial properties, immune regulation, and promotion of osteogenesis. However, a comprehensive bibliometric analysis to evaluate global scientific production in this field has not yet been conducted.

Method

We searched for publications related to nanomaterials in periodontitis and peri-implantitis using the WOSCC database. The contributions from institutions, journals, countries, and authors were assessed using VOSviewer, the bibliometrix R package, and Microsoft Excel 2019.

Results

We identified 2275 publications from 66 countries/regions focusing on nanomaterials in periodontitis and peri-implantitis, published between 1993 and 2023. China and the USA were the top contributors in this field, with 653 and 221 publications, respectively. Key topics include antibacterial properties, delivery systems, nanoparticles, and regeneration. The research focus has evolved from traditional treatments to advanced applications of multifunctional nanomaterials.

Conclusion

Significant progress has been made in the application of NMs in periodontitis and peri-implantitis from 1993 to 2023. Future research hotspots will likely focus on multifunctional nanomaterials and those adhering to good manufacturing practices (GMP).

Comparative investigation of neoadjuvant immunotherapy versus adjuvant immunotherapy in perioperative patients with cancer: a global-scale, cross-sectional, and 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 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 (R 2 =0.7500, P =0.0025) and biomarkers (R 2 =0.6505, P =0.0086) in neoadjuvant scenarios, and the tumor microenvironment (R 2 =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.

Construction of exosome-related genes risk model in kidney cell carcinoma predicts prognosis and immune therapy response

Renal cell carcinoma (RCC) is one of the most prevalent types of urological cancer. Exosomes are vesicles derived from cells and have been found to promote the development of RCC, but the potential biomarker and molecular mechanism of exosomes on RCC remain ambiguous. Here, we first screened differentially expressed exosome-related genes (ERGs) by analyzing The Cancer Genome Atlas (TCGA) database and exoRBase 2.0 database. We then determined prognosis-related ERGs (PRERGs) by univariate Cox regression analysis. Gene Dependency Score (gDS), target development level, and pathway correlation analysis were utilized to examine the importance of PRERGs. Machine learning and lasso-cox regression were utilized to screen and construct a 5-gene risk model. The risk model showed high predictive accuracy for the prognosis of patients and proved to be an independent prognostic factor in three RCC datasets, including TCGA-KIRC, E-MTAB-1980, and TCGA-KIRP datasets. Patients with high-risk scores showed worse outcomes in different clinical subgroups, revealing that the risk score is robust. In addition, we found that immune-related pathways are highly enriched in the high-risk group. Activities of immune cells were distinct in high-/low-risk groups. In independent immune therapeutic cohorts, high-risk patients show worse immune therapy responses. In summary, we identified several exosome-derived genes that might play essential roles in RCC and constructed a 5-gene risk signature to predict the prognosis of RCC and immune therapy response.

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.

Unveiling the role of YARS1 in bladder cancer: A prognostic biomarker and therapeutic target

YARS is responsible for catalysing the binding of tyrosine to its cognate tRNA and plays a crucial role in basic biosynthesis. However, its biological functions in bladder cancer remains to be proven. We analysed variations in YARS1 expression and survival in bladder cancer using multiple data sets, including TCGA-BLCA, GSE13507 and bladder cancer-specific tissue microarrays. Furthermore, we explored the biological functions of YARS1 using transcriptome data. Our findings revealed a noteworthy correlation between YARS1 and immune infiltration in bladder cancer, as determined using the XCELL algorithm and single-cell analysis. In addition, we employed the TIDE algorithm to evaluate the responsiveness of different cohorts to immune checkpoint therapy. We investigated the regulatory associations between YARS1 and various aspects of bladder cancer, including senescence, ferroptosis and stemness. Finally, we established a ceRNA network that is directly linked to the overall prognosis, YARS1 can serve as a prognostic biomarker for bladder cancer; its interaction with MYC has implications for bladder cancer cell senescence, ferroptosis and stemness. Moreover, the identified ceRNA network has potential as a therapeutic target in bladder cancer.

Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC.

Predictors of gastrointestinal complaints in patients on metformin therapy

Although being very effective in the treatment of diabetes and a few other conditions, metformin (MTF) cannot be tolerated by many patients due to gastrointestinal (GI) complaints. A number of risk factors for intolerance were identified, but many are still controversial or uninvestigated. The aim of this study was to further investigate possible risk factors for the occurrence of GI complaints in patients on MTF therapy. A cross-sectional design was used for this multicentric study on adult patients visiting 50 community pharmacies in Montenegro. The patients were surveyed by semi-structured questionnaire after a service of a pharmacist was delivered, and their drugs dispensed. Uni- and multi-variate regression methods were used for processing the data. In total 330 patients participated in the study. A higher body mass index (OR = 1.113, p = 0.003), living at a higher altitude (OR = 1.725, p = 0.000), anaemia (OR = 4.221, p = 0.008), and intestinal infection in the last 3 months (OR = 2.801, p = 0.006) increased the risk of GI complaints in patients on MTF therapy, while the use of statins was protective (OR = 0.204, p = 0.016). Each case of MTF intolerance should be carefully investigated for risk and protective factors, which could be potentially eliminated or augmented, respectively, and MTF withdrawal avoided.