Cross-Talk of Multiple Types of RNA Modification Regulators Uncovers the Tumor Microenvironment and Immune Infiltrates in Soft Tissue Sarcoma

Emerging roles of the epitranscriptome in parasitic protozoan biology and pathogenesis

RNA modifications (epitranscriptome) - such as N6-methyladenosine (m6A), 5-methylcytosine (m5C), and pseudouridine (Ψ) - modulate RNA processing, stability, interaction, and translation, thereby playing critical roles in the development, replication, virulence, metabolism, and life cycle adaptations of parasitic protozoa. Here, we summarize potential homologs of the major human RNA modification regulatory factors in parasites, outline current knowledge on how RNA modifications affect parasitic protozoa, highlight the regulation of RNA modifications and their crosstalk, and discuss current progress in exploring RNA modifications as potential drug targets. This review contributes to our understanding of epitranscriptomic regulation of parasitic protozoa biology and pathogenesis and provides new perspectives for the treatment of parasitic diseases.

Multiplex Profiling of Biomarker and Drug Uptake in Single Cells Using Microfluidic Flow Cytometry and Mass Spectrometry

To perform multiplex profiling of single cells and eliminate the risk of potential sample loss caused by centrifugation, we developed a microfluidic flow cytometry and mass spectrometry system (μCytoMS) to evaluate the drug uptake and induced protein expression at the single cell level. It involves a microfluidic chip for the alignment and purification of single cells followed by detection with laser-induced fluorescence (LIF) and inductively coupled plasma mass spectrometry (ICP-MS). Biofunctionalized nanoprobes (BioNPs), conjugating ∼3000 6-FAM-Sgc8 aptamers on a single gold nanoparticle (AuNP) (Kd = 0.23 nM), were engineered to selectively bind with protein tyrosine kinase 7 (PTK7) on target cells. PTK7 expression induced by oxaliplatin (OXA) uptake was assayed with LIF, while ICP-MS measurement of 195Pt revealed OXA uptake of the drug in individual cells, which provided further in-depth information about the drug in relation to PTK7 expression. At an ultralow flow of ∼0.043 dyn/cm2 (20 μL/min), the chip facilitates the extremely fast focusing of BioNPs labeled single cells without the need for centrifugal purification. It ensures multiplex profiling of single cells at a throughput speed of 500 cells/min as compared to 40 cells/min in previous studies. Using a machine learning algorithm to initially profile drug uptake and marker expression in tumor cell lines, μCytoMS was able to perform in situ profiling of the PTK7 response to the OXA at single-cell resolution for tests done on clinical samples from 10 breast cancer patients. It offers great potential for multiplex single-cell phenotypic analysis and clinical diagnosis.

M7G-related tumor immunity: novel insights of RNA modification and potential therapeutic targets

RNA modifications play a pivotal role in regulating cellular biology by exerting influence over distribution features and molecular functions at the post-transcriptional level. Among these modifications, N7-methylguanosine (m7G) stands out as one of the most prevalent. Over recent years, significant attention has been directed towards understanding the implications of m7G modification. This modification is present in diverse RNA molecules, including transfer RNAs, messenger RNAs, ribosomal RNAs, and other noncoding RNAs. Its regulation occurs through a series of specific methyltransferases and m7G-binding proteins. Notably, m7G modification has been implicated in various diseases, prominently across multiple cancer types. Earlier studies have elucidated the significance of m7G modification in the context of immune biology regulation within the tumor microenvironment. This comprehensive review culminates in a synthesis of findings related to the modulation of immune cells infiltration, encompassing T cells, B cells, and various innate immune cells, all orchestrated by m7G modification. Furthermore, the interplay between m7G modification and its regulatory proteins can profoundly affect the efficacy of diverse adjuvant therapeutics, thereby potentially serving as a pivotal biomarker and therapeutic target for combinatory interventions in diverse cancer types.

Deciphering the Divergent Gene Expression Landscapes of m6A/m5C/m1A Methylation Regulators in Hepatocellular Carcinoma Through Single-Cell and Bulk RNA Transcriptomic Analysis

Introduction

RNA modifications mediated by the m6A, m1A, and m5C regulatory genes are crucial for the progression of malignancy. This study aimed to explore the expression of regulator genes for m6A/m5C/m1A methylation at the single-cell level and to validate their expression in cancerous and adjacent para-cancerous liver tissues of adult patients with HCC who underwent tumor resection.

Methods

The bulk sequencing from The Cancer Genome Atlas (TCGA) database and the single-cell RNA sequencing (scRNA-seq) data obtained from the Gene Expression Omnibus (GEO) database were used to identify the dysregulated m6A/m5C/m1A genes for hepatocellular carcinoma (HCC). A real-time polymerase chain reaction (real-time PCR) was used to measure the expression of dysregulated m6A/m5C/m1A genes in collected human HCC tissues and compared with adjacent para-cancerous liver tissues. Immune cell infiltration with these significantly expressed methylation-related genes was evaluated using Timer2.0.

Results

A discrepancy in m6A/m5C/m1A gene expression was observed between bulk sequencing and scRNA-seq. The clustered heatmap of the scRNA-seq-identified dysregulated m6A/m5C/m1A genes in TCGA cohort revealed heterogeneous expression of these methylation regulators within the cancer, whereas their expression in the adjacent liver tissues was more homogeneous. The real-time PCR validated the significant overexpression of DNMT1, NSUN5, TRMT6, IGF2BP1, and IGFBP3, which were identified using scRNA-seq, and IGFBP2, which was identified using bulk sequencing. These dysregulated methylation genes are mainly correlated with the infiltration of natural killer cells.

Discussion

This study suggests that cellular diversity inside tumors contributes to the discrepancy in the expression of methylation regulator genes between traditional bulk sequencing and scRNA-seq. This study identified five regulatory genes that will be the focus of further studies regarding the function of m6A/m5C/m1A in HCC.

A tumor-infiltrating immune cells-related pseudogenes signature based on machine-learning predicts outcomes and immunotherapy responses in ovarian cancer

Previous researches have provided evidence for the significant involvement of pseudogenes in immune-related functions across different types of cancer. However, the mechanisms by which pseudogenes regulate immunity in ovarian cancer (OV) and their potential impact on clinical outcomes remain unclear. To address this gap in knowledge, our study utilized a novel computational framework to analyze a total of 491 samples from three public datasets. We employed a combination of 10 machine-learning algorithms to construct a signature known as the tumor-infiltrating immune cells-related pseudogenes signature (TIICPS). The TIICPS, consisting of 12 pseudogenes, demonstrated independent prognostic value for overall survival, surpassing conventional clinical traits, 62 published signatures, and TP53 and BRCA mutation status in three cohorts. Patients with low TIICPS exhibited heightened immune-related pathways, intricate genomic alterations, substantial immune infiltration, and greater potential for immunotherapy efficacy. Consequently, TIICPS holds promise as a predictive tool for prognosis and immunotherapy response in ovarian cancer.

PIK3CD correlates with prognosis, epithelial-mesenchymal transition and tumor immune infiltration in breast carcinoma

BACKGROUND

Breast carcinoma (BRCA) is one of the most common, fatal, and aggressive cancers, with increasing morbidity that has a major impact on human health. PIK3CD appears to have important roles in the beginning and advancement of various forms of human cancer, according to mounting data. However,the particular role and mechanism of PIK3CD in BRCA remains not fully identified.

METHODOLOGY

The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/ ), Genotype-Tissue Expression (GTEx) data and the UCSC Xena browser ( https://xenabrowser.net ) data were used in this study's initial pan-cancer analysis of PIK3CD expression and prognosis. Circular RNAs (circRNAs) that regulated the expression of PIK3CD were subsequently found using a combination of in silico investigations of expression, correlation, and survival. Measurements of PIK3CD expression and an analysis of the in vitro function of PIK3CD in BRCA cells were performed using real-time RT-PCR, Western blotting and Transwell assays.

RESULTS

In BRCA GLI2, RAB32, LAMB1, MGAT2, ITGA8, CHF, COL6A3 and PRRX1-miR-30b-5p axis was identified as the most likely upstream CircRNA-related route of PIK3CD. PIK3CD was correlated with the expression of EMT markers. The PIK3CD cDNA improved the capacity for invasion and migration. The expression of PIK3CD was linked to some of the m1A/m5C/m6A regulators. Additionally, it was discovered that the expression of PIK3CD was found to be highly connected to the expression of immunological checkpoints, immune cell biomarkers, and tumor immune cell invasion.

CONCLUSIONS

Our findings reveal that PIK3CD expression is associated with prognosis, EMT, and tumor immune infiltration in BRCA patients.

A novel m6A/m5C/m1A score signature to evaluate prognosis and its immunotherapy value in colon cancer patients

BACKGROUND

Colon cancer features strong heterogeneity and invasiveness, with high incidence and mortality rates. Recently, RNA modifications involving m6A, m5C, and m1A play a vital part in tumorigenesis and immune cell infiltration. However, integrated analysis among various RNA modifications in colon cancer has not been performed.

METHODS

RNA-seq profiling, clinical data and mutation data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. We first explored the mutation status and expression levels of m6A/m5C/m1A regulators in colon cancer. Then, different m6A/m5C/m1A clusters and gene clusters were identified by consensus clustering analysis. We further constructed and validated a scoring system, which could be utilized to accurately assess the risk of individuals and guide personalized immunotherapy. Finally, m6A/m5C/m1A regulators were validated by immunohistochemical staining and RT-qPCR.

RESULTS

In our study, three m6A/m5C/m1A clusters and gene clusters were identified. Most importantly, we constructed a m6A/m5C/m1A scoring system to assess the clinical risk of the individuals. Besides, the prognostic value of the score was validated with three independent cohorts. Moreover, the level of the immunophenoscore of the low m6A/m5C/m1A score group increased significantly with CTLA-4/PD-1 immunotherapy. Finally, we validated that the mRNA and protein expression of VIRMA and DNMT3B increased in colon cancer tissues.

CONCLUSIONS

We constructed and validated a stable and powerful m6A/m5C/m1A score signature to assess the survival outcomes and immune infiltration characteristics of colon cancer patients, which further guides optimization of personalized treatment, making it valuable for clinical translation and implementation.

Necroptosis in the sarcoma immune microenvironment: From biology to therapy

Apoptosis resistance remains a major obstacle to treatment failure in sarcoma. Necroptosis is a caspase-independent programmed cell death, investigated as a novel strategy to eradicate anti-apoptotic tumor cells. The process is mediated by the receptor-interacting proteins kinase family and mixed lineage kinase domain-like proteins, which is morphologically similar to necrosis. Recent studies suggest that necroptosis in the tumor microenvironment has pro- or anti-tumor effects on immune response and cancer development. Necroptosis-related molecules display a remarkable value in prognosis prediction and therapeutic response evaluation of sarcoma. Furthermore, the induction of tumor necroptosis has been explored as a feasible therapeutic strategy against sarcoma and to synergize with immunotherapy. This review discusses the dual roles of necroptosis in the immune microenvironment and tumor progression, and explores the potential of necroptosis as a new target for sarcoma treatment.

RNA epigenetic modifications in ovarian cancer: The changes, chances, and challenges

Ovarian cancer (OC) is the most common female cancer worldwide. Patients with OC have high mortality because of its complex and poorly understood pathogenesis. RNA epigenetic modifications, such as m6 A, m1 A, and m5 C, are closely associated with the occurrence and development of OC. RNA modifications can affect the stability of mRNA transcripts, nuclear export of RNAs, translation efficiency, and decoding accuracy. However, there are few overviews that summarize the link between m6 A RNA modification and OC. Here, we discuss the molecular and cellular functions of different RNA modifications and how their regulation contributes to the pathogenesis of OC. By improving our understanding of the role of RNA modifications in the etiology of OC, we provide new perspectives for their use in OC diagnosis and treatment. This article is categorized under: RNA Processing > RNA Editing and Modification RNA in Disease and Development > RNA in Disease.

Integration analysis of senescence-related genes to predict prognosis and immunotherapy response in soft-tissue sarcoma: evidence based on machine learning and experiments

Background: Soft tissue sarcoma (STS) is the malignancy that exhibits remarkable histologic diversity. The diagnosis and treatment of STS is currently challenging, resulting in a high lethality. Chronic inflammation has also been identified as a key characteristic of tumors, including sarcomas. Although senescence plays an important role in the progression of various tumors, its molecular profile remains unclear in STS. Methods: We identified the senescence-related genes (SRGs) in database and depicted characteristics of genomic and transcriptomic profiling using cohort within TCGA and GEO database. In order to investigate the expression of SRGs in different cellular subtypes, single-cell RNA sequencing data was applied. The qPCR and our own sequencing data were utilized for further validation. We used unsupervised consensus clustering analysis to establish senescence-related clusters and subtypes. A senescence scoring system was established by using principal component analysis (PCA). The evaluation of clinical and molecular characteristics was conducted among distinct groups. Results: These SRGs showed differences in SCNV, mutation and mRNA expression in STS tissues compared to normal tissues. Across several cancer types, certain shared features of SRGs were identified. Several SRGs closely correlated with immune cell infiltration. Four clusters related to senescence and three subtypes related to senescence, each with unique clinical and biological traits, were established. The senescence scoring system exhibited effectiveness in predicting outcomes, clinical traits, infiltrations of immune cells and immunotherapy responses. Conclusion: Overall, the current study provided a comprehensive review of molecular profiling for SRGs in STS. The SRGs based clustering and scoring model could help guiding the clinical management of STS.

Deciphering the role of NETosis-related signatures in the prognosis and immunotherapy of soft-tissue sarcoma using machine learning

Background: Soft-tissue sarcomas (STSs) are a rare type of cancer, accounting for about 1% of all adult cancers. Treatments for STSs can be difficult to implement because of their diverse histological and molecular features, which lead to variations in tumor behavior and response to therapy. Despite the growing importance of NETosis in cancer diagnosis and treatment, researches on its role in STSs remain limited compared to other cancer types. Methods: The study thoroughly investigated NETosis-related genes (NRGs) in STSs using large cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine Recursive Feature Elimination (SVM-RFE) were employed for screening NRGs. Utilizing single-cell RNA-seq (scRNA-seq) dataset, we elucidated the expression profiles of NRGs within distinct cellular subpopulations. Several NRGs were validated by quantitative PCR (qPCR) and our proprietary sequencing data. To ascertain the impact of NRGs on the sarcoma phenotype, we conducted a series of in vitro experimental investigations. Employing unsupervised consensus clustering analysis, we established the NETosis clusters and respective NETosis subtypes. By analyzing DEGs between NETosis clusters, an NETosis scoring system was developed. Results: By comparing the outcomes obtained from LASSO regression analysis and SVM-RFE, 17 common NRGs were identified. The expression levels of the majority of NRGs exhibited notable dissimilarities between STS and normal tissues. The correlation with immune cell infiltration were demonstrated by the network comprising 17 NRGs. Patients within various NETosis clusters and subtypes exhibited different clinical and biological features. The prognostic and immune cell infiltration predictive capabilities of the scoring system were deemed efficient. Furthermore, the scoring system demonstrated potential for predicting immunotherapy response. Conclusion: The current study presents a systematic analysis of NETosis-related gene patterns in STS. The results of our study highlight the critical role NRGs play in tumor biology and the potential for personalized therapeutic approaches through the application of the NETosis score model in STS patients.

Vital roles of m5C RNA modification in cancer and immune cell biology

RNA modification plays an important role in epigenetics at the posttranscriptional level, and 5-methylcytosine (m5C) has attracted increasing attention in recent years due to the improvement in RNA m5C site detection methods. By influencing transcription, transportation and translation, m5C modification of mRNA, tRNA, rRNA, lncRNA and other RNAs has been proven to affect gene expression and metabolism and is associated with a wide range of diseases, including malignant cancers. RNA m5C modifications also substantially impact the tumor microenvironment (TME) by targeting different groups of immune cells, including B cells, T cells, macrophages, granulocytes, NK cells, dendritic cells and mast cells. Alterations in immune cell expression, infiltration and activation are highly linked to tumor malignancy and patient prognosis. This review provides a novel and holistic examination of m5C-mediated cancer development by examining the exact mechanisms underlying the oncogenicity of m5C RNA modification and summarizing the biological effects of m5C RNA modification on tumor cells as well as immune cells. Understanding methylation-related tumorigenesis can provide useful insights for the diagnosis as well as the treatment of cancer.

Modification of m5C regulators in sarcoma can guide different immune infiltrations as well as immunotherapy

Background

Recent studies have found that 5-methylcytosine (m5C) modulators are associated with the prognosis and treatment of cancer. However, the relevance of m5C modulators in sarcoma prognosis and the tumour microenvironment is unclear.

Methods

We selected 15 m5C regulators and performed unsupervised clustering to identify m5C modification patterns and differentially expressed genes associated with the m5C phenotype in The Cancer Genome Atlas (TCGA) sarcomas. The extent of immune cell infiltration in different clustering groups was explored using single-sample gene set enrichment analysis and estimation algorithms. A principal component analysis algorithm-based m5C scoring protocol was performed to assess the m5C modification patterns of individual tumors.

Results

We identified two distinct m5C modification patterns in the TCGA sarcoma cohort, which possess different clinical outcomes and biological processes. Tumour microenvironment analysis revealed two groups of immune infiltration patterns highly consistent with m5C modification patterns, classified as immune inflammatory and immune desert types. We constructed m5C scores and found that high m5C scores were closely associated with leiomyosarcoma and other subtypes, and were associated with poorer prognosis, lower PD-L1 expression, and poorer immunotherapy outcomes. The best application was validated against the m5C database.

Conclusion

We constructed an m5C score for sarcoma based on the TCGA database and identified a poorer prognosis in the high m5c score group. The stability and good prognostic predictive power of the m5C score was verified by an external database. We found that sarcomas in the low m5C score group may have a better response to immunotherapy.

Identification and validation of RNA methylation-related alternative splicing gene signature for low-grade glioma to predict survival and immune landscapes

BACKGROUND

Low-grade glioma (LGG) is a crucial pathological type of glioma. The present study aimed to explore multiple RNA methylation regulator-related AS events and investigate their prognostic values in LGG.

METHODS

The prognostic model for low-grade glioma was established using the LASSO regression analysis. To validate prognostic value, we performed Kaplan-Maier survival analysis, ROC curves and nomograms. The ESTIMATE algorithm, the CIBERSORT algorithm and the ssGSEA algorithm were utilized to explore the role of the immune microenvironment in LGG. Subsequently, we then used GO, KEGG and GSEA enrichment analysis to explore the functional roles of these genes. In addition, we employed the GDSC database to screen potential chemotherapeutic agents.

RESULTS

Eight RNA methylation related AS events were involved in construct a survival and prognosis model, which had good ability of independent prediction for patients with LGG. Patients in the high-risk group had shorter life expectancy and higher mortality, while patients in the low-risk group had a better prognosis. We constructed a nomogram which showed an excellent predictive performance for individual OS. The risk score exhibited a close correlation with some immune cells and expression of immune checkpoints. Patients in high-risk group were characterized by immunosuppressive microenvironment and poor response to immunotherapy, and were sensitive to more chemotherapeutic drugs. Pathway and functional enrichment analyses further confirmed that significant differences existed in immune landscape between the two subgroups.

CONCLUSION

The prognostic RNA methylation-related alternative splicing signature constructed could constitute a promising prognostic biomarker, which could serve to optimize treatment regimens.