Critical roles and clinical perspectives of RNA methylation in cancer.

RNA modification, especially RNA methylation, is a critical posttranscriptional process influencing cellular functions and disease progression, accounting for over 60% of all RNA modifications. It plays a significant role in RNA metabolism, affecting RNA processing, stability, and translation, thereby modulating gene expression and cell functions essential for proliferation, survival, and metastasis. Increasing studies have revealed the disruption in RNA metabolism mediated by RNA methylation has been implicated in various aspects of cancer progression, particularly in metabolic reprogramming and immunity. This disruption of RNA methylation has profound implications for tumor growth, metastasis, and therapy response. Herein, we elucidate the fundamental characteristics of RNA methylation and their impact on RNA metabolism and gene expression. We highlight the intricate relationship between RNA methylation, cancer metabolic reprogramming, and immunity, using the well-characterized phenomenon of cancer metabolic reprogramming as a framework to discuss RNA methylation's specific roles and mechanisms in cancer progression. Furthermore, we explore the potential of targeting RNA methylation regulators as a novel approach for cancer therapy. By underscoring the complex mechanisms by which RNA methylation contributes to cancer progression, this review provides a foundation for developing new prognostic markers and therapeutic strategies aimed at modulating RNA methylation in cancer treatment.

Comprehensive analysis of a tryptophan metabolism-related model in the prognostic prediction and immune status for clear cell renal carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is characterized as one of the most common types of urological cancer with high degrees of malignancy and mortality. Due to the limited effectiveness of existing traditional therapeutic methods and poor prognosis, the treatment and therapy of advanced ccRCC patients remain challenging. Tryptophan metabolism has been widely investigated because it significantly participates in the malignant traits of multiple cancers. The functions and prognostic values of tryptophan metabolism-related genes (TMR) in ccRCC remain virtually obscure. METHODS: We employed the expression levels of 40 TMR genes to identify the subtypes of ccRCC and explored the clinical characteristics, prognosis, immune features, and immunotherapy response in the subtypes. Then, a model was constructed for the prediction of prognosis based on the differentially expressed genes (DEGs) in the subtypes from the TCGA database and verified using the ICGC database. The prediction performance of this model was confirmed by the receiver operating characteristic (ROC) curves. The relationship of Risk Score with the infiltration of distinct tumor microenvironment cells, the expression profiles of immune checkpoint genes, and the treatment benefits of immunotherapy and chemotherapy drugs were also investigated. RESULTS: The two subtypes revealed dramatic differences in terms of clinical characteristics, prognosis, immune features, and immunotherapy response. The constructed 6-gene-based model showed that the high Risk Score was significantly connected to poor overall survival (OS) and advanced tumor stages. Furthermore, increased expression of CYP1B1, KMO, and TDO2 was observed in ccRCC tissues at the translation levels, and an unfavorable prognosis for these patients was also found. CONCLUSION: We identified 2 molecular subtypes of ccRCC based on the expression of TMR genes and constructed a prognosis-related model that may be used as a powerful tool to guide the prediction of ccRCC prognosis and personalized therapy. In addition, CYP1B1, KMO, and TDO2 can be regarded as the risk prognostic genes for ccRCC.

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma.

BACKGROUND: Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions. METHODS: RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses. RESULTS: ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients. CONCLUSIONS: The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

Prognostic prediction and immunotherapy response analysis of the fatty acid metabolism-related genes in clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is a common urinary cancer. Although diagnostic and therapeutic approaches for ccRCC have been improved, the survival outcomes of patients with advanced ccRCC remain unsatisfactory. Fatty acid metabolism (FAM) has been increasingly recognized as a critical modulator of cancer development. However, the significance of the FAM in ccRCC remains unclear. Herein, we explored the function of a FAM-related risk score in the stratification and prediction of treatment responses in patients with ccRCC. Methods: First, we applied an unsupervised clustering method to categorize patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets into subtypes and retrieved FAM-related genes from the MSigDB database. We discern differentially expressed genes (DEGs) among different subtypes. Then, we applied univariate Cox regression analysis followed by least absolute shrinkage and selection operator (LASSO) linear regression based on DEGs expression to establish a FAM-related risk score for ccRCC. Results: We stratified the three ccRCC subtypes based on FAM-related genes with distinct overall survival (OS), clinical features, immune infiltration patterns, and treatment sensitivities. We screened nine genes from the FAM-related DEGs in the three subtypes to establish a risk prediction model for ccRCC. Nine FAM-related genes were differentially expressed in the ccRCC cell line ACHN compared to the normal kidney cell line HK2. High-risk patients had worse OS, higher genomic heterogeneity, a more complex tumor microenvironment (TME), and elevated expression of immune checkpoints. This phenomenon was validated in the ICGC cohort. Conclusion: We constructed a FAM-related risk score that predicts the prognosis and therapeutic response of ccRCC. The close association between FAM and ccRCC progression lays a foundation for further exploring FAM-related functions in ccRCC.

Evolving cognition of the JAK-STAT signaling pathway: autoimmune disorders and cancer.

The Janus kinase (JAK) signal transducer and activator of transcription (JAK-STAT) pathway is an evolutionarily conserved mechanism of transmembrane signal transduction that enables cells to communicate with the exterior environment. Various cytokines, interferons, growth factors, and other specific molecules activate JAK-STAT signaling to drive a series of physiological and pathological processes, including proliferation, metabolism, immune response, inflammation, and malignancy. Dysregulated JAK-STAT signaling and related genetic mutations are strongly associated with immune activation and cancer progression. Insights into the structures and functions of the JAK-STAT pathway have led to the development and approval of diverse drugs for the clinical treatment of diseases. Currently, drugs have been developed to mainly target the JAK-STAT pathway and are commonly divided into three subtypes: cytokine or receptor antibodies, JAK inhibitors, and STAT inhibitors. And novel agents also continue to be developed and tested in preclinical and clinical studies. The effectiveness and safety of each kind of drug also warrant further scientific trials before put into being clinical applications. Here, we review the current understanding of the fundamental composition and function of the JAK-STAT signaling pathway. We also discuss advancements in the understanding of JAK-STAT-related pathogenic mechanisms; targeted JAK-STAT therapies for various diseases, especially immune disorders, and cancers; newly developed JAK inhibitors; and current challenges and directions in the field.

STXBP3 and GOT2 predict immunological activity in acute allograft rejection.

Background: Acute allograft rejection (AR) following renal transplantation contributes to chronic rejection and allograft dysfunction. The current diagnosis of AR remains dependent on renal allograft biopsy which cannot immediately detect renal allograft injury in the presence of AR. In this study, sensitive biomarkers for AR diagnosis were investigated and developed to protect renal function. Methods: We analyzed pre- and postoperative data from five databases combined with our own data to identify the key differently expressed genes (DEGs). Furthermore, we performed a bioinformatics analysis to determine the immune characteristics of DEGs. The expression of key DEGs was further confirmed using the real-time quantitative PCR (RT-qPCR), enzyme-linked immunosorbent assay (ELISA), and immunohistochemical (IHC) staining in patients with AR. ROC curves analysis was used to estimate the performance of key DEGs in the early diagnosis of AR. Results: We identified glutamic-oxaloacetic transaminase 2 (GOT2) and syntaxin binding protein 3 (STXBP3) as key DEGs. The higher expression of STXBP3 and GOT2 in patients with AR was confirmed using RT-qPCR, ELISA, and IHC staining. ROC curve analysis also showed favorable values of STXBP3 and GOT2 for the diagnosis of early stage AR. Conclusions: STXBP3 and GOT2 could reflect the immunological status of patients with AR and have strong potential for the diagnosis of early-stage AR.

The emerging potentials of lncRNA DRAIC in human cancers.

Long non-coding RNA (lncRNA) is a subtype of noncoding RNA that has more than 200 nucleotides. Numerous studies have confirmed that lncRNA is relevant during multiple biological processes through the regulation of various genes, thus affecting disease progression. The lncRNA DRAIC, a newly discovered lncRNA, has been found to be abnormally expressed in a variety of diseases, particularly cancer. Indeed, the dysregulation of DRAIC expression is closely related to clinicopathological features. It was also reported that DRAIC is key to biological functions such as cell proliferation, autophagy, migration, and invasion. Furthermore, DRAIC is of great clinical significance in human disease. In this review, we discuss the expression signature, clinical characteristics, biological functions, relevant mechanisms, and potential clinical applications of DRAIC in several human diseases.

The integrated comprehension of lncRNA HOXA-AS3 implication on human diseases.

Long non-coding RNA (lncRNA) is a non-protein-coding RNA with a length of more than 200 nucleotides. Studies have shown that lncRNAs have vital impacts on various pathological processes and participate in the development of human diseases, usually through acting as competing endogenous RNAs to modulate miRNA expression and biological functions. lncRNA HOXA Cluster Antisense RNA 3 (HOXA-AS3) was a newly discovered lncRNA and has been demonstrated to be abnormally expressed in many diseases. Moreover, HOXA-AS3 expression was closely correlated with the clinicopathologic characteristics in cancer patients. In addition, HOXA-AS3 exhibited significant properties in regulating several biological processes, including cell proliferation, invasion, and migration. Furthermore, HOXA-AS3 has provided promising values in the diagnosis, prognosis, and therapeutic strategies of several diseases such as liver cancer, glioma, lung cancer, oral cancer, gastric cancer, and even atherosclerosis. In this review, we discuss the abnormal expression of HOXA-AS3 in several human disorders and some pathobiological processes and its clinical characteristics, followed by a summary of HOXA-AS3 functions, regulatory mechanisms, and clinical application potential.

Emerging Roles and Mechanisms of lncRNA FOXD3-AS1 in Human Diseases.

Numerous long noncoding RNAs (lncRNAs) have been identified as powerful regulators of human diseases. The lncRNA FOXD3-AS1 is a novel lncRNA that was recently shown to exert imperative roles in the initialization and progression of several diseases. Emerging studies have shown aberrant expression of FOXD3-AS1 and close correlation with pathophysiological traits of numerous diseases, particularly cancers. More importantly, FOXD3-AS1 was also found to ubiquitously impact a range of biological functions. This study aims to summarize the expression, associated clinicopathological features, major functions and molecular mechanisms of FOXD3-AS1 in human diseases and to explore its possible clinical applications.

Review of LINC00707: A Novel LncRNA and Promising Biomarker for Human Diseases.

Long noncoding RNAs (lncRNAs) are a major type of noncoding RNA greater than 200 nucleotides in length involved in important regulatory processes. Abnormal expression of certain lncRNAs contributes to the pathogenesis of multiple diseases, including cancers. The lncRNA LINC00707 is located on chromosome 10p14 and is abnormally expressed in numerous disease types, and particularly in several types of cancer. High LINC00707 levels mediate a series of biological functions, including cell proliferation, apoptosis, metastasis, invasion, cell cycle arrest, inflammation, and even osteogenic differentiation. In this review, we discuss the main functions and underlying mechanisms of LINC00707 in different diseases and describe promising applications of LINC00707 in clinical settings.