Recent progress in retinoblastoma: Pathogenesis, presentation, diagnosis and management.

Retinoblastoma, the primary ocular malignancy in pediatric patients, poses a substantial threat to mortality without prompt and effective management. The prognosis for survival and preservation of visual acuity hinges upon the disease severity at the time of initial diagnosis. Notably, retinoblastoma has played a crucial role in unraveling the genetic foundations of oncogenesis. The process of tumorigenesis commonly begins with the occurrence of biallelic mutation in the RB1 tumor suppressor gene, which is then followed by a cascade of genetic and epigenetic alterations that correspond to the clinical stage and pathological features of the tumor. The RB1 gene, recognized as a tumor suppressor, encodes the retinoblastoma protein, which plays a vital role in governing cellular replication through interactions with E2F transcription factors and chromatin remodeling proteins. The diagnosis and treatment of retinoblastoma necessitate consideration of numerous factors, including disease staging, germline mutation status, family psychosocial factors, and the resources available within the institution. This review has systematically compiled and categorized the latest developments in the diagnosis and treatment of retinoblastoma which enhanced the quality of care for this pediatric malignancy.

A gene signature linked to fibroblast differentiation for prognostic prediction of mesothelioma.

BACKGROUND: Malignant mesothelioma is a type of infrequent tumor that is substantially related to asbestos exposure and has a terrible prognosis. We tried to produce a fibroblast differentiation-related gene set for creating a novel classification and prognostic prediction model of MESO. METHOD: Three databases, including NCBI-GEO, TCGA, and MET-500, separately provide single-cell RNA sequencing data, bulk RNA sequencing profiles of MESO, and RNA sequencing information on bone metastatic tumors. Dimensionality reduction and clustering analysis were leveraged to acquire fibroblast subtypes in the MESO microenvironment. The fibroblast differentiation-related genes (FDGs), which were associated with survival and subsequently utilized to generate the MESO categorization and prognostic prediction model, were selected in combination with pseudotime analysis and survival information from the TCGA database. Then, regulatory network was constructed for each MESO subtype, and candidate inhibitors were predicted. Clinical specimens were collected for further validation. RESULT: A total of six fibroblast subtypes, three differentiation states, and 39 FDGs were identified. Based on the expression level of FDGs, three MESO subtypes were distinguished in the fibroblast differentiation-based classification (FDBC). In the multivariate prognostic prediction model, the risk score that was dependent on the expression level of several important FDGs, was verified to be an independently effective prognostic factor and worked well in internal cohorts. Finally, we predicted 24 potential drugs for the treatment of MESO. Moreover, immunohistochemical staining and statistical analysis provided further validation. CONCLUSION: Fibroblast differentiation-related genes (FDGs), especially those in low-differentiation states, might participate in the proliferation and invasion of MESO. Hopefully, the raised clinical subtyping of MESO would provide references for clinical practitioners.

Sequencing technology as a major impetus in the advancement of studies into rheumatism: A bibliometric study.

Background: Rheumatism covers a wide range of diseases with complex clinical manifestations and places a tremendous burden on humans. For many years, our understanding of rheumatism was seriously hindered by technology constraints. However, the increasing application and rapid advancement of sequencing technology in the past decades have enabled us to study rheumatism with greater accuracy and in more depth. Sequencing technology has made huge contributions to the field and is now an indispensable component and powerful tool in the study of rheumatism. Methods: Articles on sequencing and rheumatism, published from 1 January 2000 to 25 April 2022, were retrieved from the Web of Science™ (Clarivate™, Philadelphia, PA, USA) database. Bibliometrix, the open-source tool, was used for the analysis of publication years, countries, authors, sources, citations, keywords, and co-words. Results: The 1,374 articles retrieved came from 62 countries and 350 institutions, with a general increase in article numbers during the last 22 years. The leading countries in terms of publication numbers and active cooperation with other countries were the USA and China. The most prolific authors and most popular documents were identified to establish the historiography of the field. Popular and emerging research topics were assessed by keywords and co-occurrence analysis. Immunological and pathological process in rheumatism, classification, risks and susceptibility, and biomarkers for diagnosis were among the hottest themes for research. Conclusions: Sequencing technology has been widely applied in the study of rheumatism and propells research in the area of discovering novel biomarkers, related gene patterns and physiopathology. We suggest that further efforts be made to advance the study of genetic patterns related to rheumatic susceptibility, pathogenesis, classification and disease activity, and novel biomarkers.

Pan-cancer analysis reveals signal transducer and activator of transcription (STAT) gene family as biomarkers for prognostic prediction and therapeutic guidance.

Background: The signal transducer and activator of transcription (STAT) gene family have been widely found to regulate cell proliferation, differentiation, apoptosis, and angiogenesis through complex signaling pathways, and thus impacting tumor formation and development in different types of tumor. However, the roles of STATs on prognostic prediction and therapeutic guidance in pan-cancer remain unexplored. Materials and Methods: The dataset of 33 types of TCGA tumor, para-carcinoma and normal tissues, was obtained from the UCSC Xena database, including the gene expression profiles in the formats of FPKM value, demographic characteristics, clinical information, and survival data of STATs. Differential expression and co-expression analyses, WGCNA, clinical relevance analysis, immune subtype analysis, tumor stemness analysis, tumor purity analysis, immune infiltration analysis, immunotherapy related analysis, tumor mutation related analysis, and drug sensitivity analysis were performed by R software. Results: Differential expression of STAT1 was found between normal and BRCA tissues (p < 0.001, log2FC = 0.895). Additionally, the strongest correlation among STATs lied between STAT1 and STAT2 (correlation coefficient = 0.6). Moreover, high expression levels of STAT1 (p = 0.031) were revealed to be notably correlated with poor prognosis in KIRP. In addition, STAT1 expressed the highest value in immune subtypes C1, C2, C3, and C6 in LUAD. What's more, strong negative correlations were demonstrated between expression of STAT6 and mDNAss and mRNAss of TGCT. Additionally, STAT4 expression was characterized to be significantly negatively correlated with tumor purity of the majority of cancer types. Moreover, STAT1 and STAT3 were shown to be generally high-expressed in pan-cancer myeloid cells, and STATs all had positive correlation with the infiltration of the majority of immune cells. In addition, STATs were revealed to be closely linked with immunotherapy response. What's more, STAT4 expression was identified to have a strong negative correlation with TMB value in DLBC. Last but not least, positive correlations were accessed between STAT5 and sensitivity of Nelarabine (cor = 0.600, p < 0.001). Conclusion: In the present study, we identified STATs as biomarkers for prognostic prediction and therapeutic guidance in pan-cancer. Hopefully our findings could provide a valuable reference for future STATs research and clinical applications.

Effects of intestinal microbes on rheumatic diseases: A bibliometric analysis.

Background: Rheumatic diseases (RD) are a group of multi-system inflammatory autoimmune diseases whose causes are still under study. In the past few decades, researchers have found traces of the association between rheumatic diseases and intestinal microbiota, which can partially explain the pathogenesis of rheumatic diseases. We aimed to describe the research trend and main divisions on how gut flora interreacts with rheumatic diseases, and discussed about the possible clinical applications. Methods: We analyzed bibliometric data from the Web of Science core collection (dated 15th May 2022). Biblioshiny R language software packages (bibliometrix) were used to obtain the annual publication and citations, core sources according to Bradford's law, and country collaboration map. We designed and verified the keyword co-occurrence network and strategic diagram with the help of VOSviewer and CiteSpace, subdivided the research topic into several themes and identified research dimensions. The tables of most local cited documents and core sources were processed manually. Furthermore, the Altmetric Attention Score and the annual Altmetric Top 100 were applied to analyze the annual publication and citation. Results: From a total of 541 documents, we found that the overall trend of annual publication and citation is increasing. The major research method is to compare the intestinal microbial composition of patients with certain rheumatic disease and that of the control group to determine microbial alterations related to the disease's occurrence and development. According to Bradford's law, the core sources are Arthritis and Rheumatology, Annals of the Rheumatic Diseases, Current Opinion in Rheumatology, Nutrients, Rheumatology, and Journal of Rheumatology. Since 1976, 101 countries or regions have participated in studies of rheumatology and intestinal microbes. The United States ranks at the top and has the broadest academic association with other countries. Five themes were identified, including the pivotal role of inflammation caused by intestinal bacteria in the rheumatic pathogenesis, the close relationship between rheumatic diseases and inflammatory bowel disease, immunoregulation mechanism as a mediator of the interaction between rheumatic diseases and gut flora, dysbiosis and decreased diversity in intestine of patients with rheumatic diseases, and the influence of oral flora on rheumatic diseases. Additionally, four research dimensions were identified, including pathology, treatment, disease, and experiments. Conclusion: Studies on rheumatic diseases and the intestinal microbiota are growing. Attention should be paid to the mechanism of their interaction, such as the microbe-immune-RD crosstalk. Hopefully, the research achievements can be applied to diseases' prevention, diagnosis, and treatment, and our work can contribute to the readers' future research.