The microRNA target site landscape is a novel molecular feature associating alternative polyadenylation with immune evasion activity in breast cancer

Classification of cancer types based on microRNA expression using a hybrid radial basis function and particle swarm optimization algorithm

The diagnosis and treatment of cancer is one of the most challenging aspects of the medical profession, despite advances in disease diagnosis. MicroRNAs are small noncoding RNA molecules involved in regulating gene expression and are associated with several cancer types. Therefore, the analysis of microRNA data has become one of the most important areas of cancer research in recent years. This paper presents an improved method for cancer-type classification based on microRNA expression data using a hybrid radial basis function (RBF) and particle swarm optimization (PSO) algorithm. Two datasets containing microRNA information were used, and preprocessing and normalization operations were performed on the raw data. Feature selection was carried out by using the PSO algorithm, which can identify the most relevant and informative features in the data along with helping to prioritize them. Using a PSO algorithm for feature selection is an effective approach to microRNA analysis. This enhances the accuracy and reliability of cancer-type classifications based on microRNA expression data. In the proposed method, we, respectively, achieved an accuracy of 0.95% and 0.91% on both datasets, with an average of 0.93%, using an improved RBF neural network classifier. These results demonstrate that the proposed method outperforms previous works. RESEARCH HIGHLIGHTS: To enhance the accuracy of cancer-type classifications based on microRNA expression data. We present a minimal feature selection method using particle swarm optimization to reduce computational load & radial basis function to improve accuracy.

Alternative Polyadenylation Regulatory Factors Signature for Survival Prediction in Kidney Renal Cell Carcinoma

Background

Alternative polyadenylation (APA) plays a vital regulatory role in various diseases. It is widely accepted that APA is regulated by APA regulatory factors.

Objective

Whether APA regulatory factors affect the prognosis of renal cell carcinoma remains unclear, and this is the main topic of this study.

Methods

We downloaded the transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database. We used the Lasso regression system to construct an APA model for analyzing the relationship between common APA regulatory factors and renal cell carcinoma. We also validated our APA model using independent GEO datasets (GSE29609, GSE76207).

Results

It was found that the expression levels of 5 APA regulatory factors (CPSF1, CPSF2, CSTF2, PABPC1, and PABPC4) were significantly associated with tumor gene mutation burden (TMB) score in renal clear cell carcinoma, and the risk score constructed using the expression level of 5 key APA regulatory factors could be used to predict the outcome of renal clear cell carcinoma. The TMB score is associated with the remodeling of the immune microenvironment.

Conclusions

By identifying key APA regulatory factors in renal cell carcinoma and constructing risk scores for key APA regulatory factors, we showed that key APA regulators affect prognosis of renal clear cell carcinoma patients. In addition, the risk score level is associated with TMB, indicating that APA may affect the efficacy of immunotherapy through immune microenvironment-related genes. This helps us better understand the mRNA processing mechanism of renal clear cell carcinoma.

Malignant A-to-I RNA editing by ADAR1 drives T cell acute lymphoblastic leukemia relapse via attenuating dsRNA sensing

Leukemia-initiating cells (LICs) are regarded as the origin of leukemia relapse and therapeutic resistance. Identifying direct stemness determinants that fuel LIC self-renewal is critical for developing targeted approaches. Here, we show that the RNA-editing enzyme ADAR1 is a crucial stemness factor that promotes LIC self-renewal by attenuating aberrant double-stranded RNA (dsRNA) sensing. Elevated adenosine-to-inosine editing is a common attribute of relapsed T cell acute lymphoblastic leukemia (T-ALL) regardless of molecular subtype. Consequently, knockdown of ADAR1 severely inhibits LIC self-renewal capacity and prolongs survival in T-ALL patient-derived xenograft models. Mechanistically, ADAR1 directs hyper-editing of immunogenic dsRNA to avoid detection by the innate immune sensor melanoma differentiation-associated protein 5 (MDA5). Moreover, we uncover that the cell-intrinsic level of MDA5 dictates the dependency on the ADAR1-MDA5 axis in T-ALL. Collectively, our results show that ADAR1 functions as a self-renewal factor that limits the sensing of endogenous dsRNA. Thus, targeting ADAR1 presents an effective therapeutic strategy for eliminating T-ALL LICs.

Pan-cancer analysis of NUDT21 and its effect on the proliferation of human head and neck squamous cell carcinoma

BACKGROUND

Based on bioinformatics research of NUDT21 in pan-cancer, we aimed to clarify the mechanism of NUDT21 in HHNC by experiment.

METHODS

The correlation between differential expression of NUDT21 in pan-cancer and survival prognosis, genomic instability, tumor stemness, DNA repair, RNA methylation and with immune microenvironment were analyzed by the application of different pan-cancer analysis web databases. In addition, immunohistochemistry staining and genetic detection of NUDT21 in HHNCC tumor tissues by immunohistochemistry and qRT-PCR. Then, through in vitro cell experiments, NUDT21 was knocked down by lentivirus to detect the proliferation, cycle, apoptosis of FaDu and CNE-2Z cells, and finally by PathScan intracellular signaling array reagent to detect the apoptotic protein content.

RESULTS

Based on the pan-cancer analysis, we found that elevated expression of NUDT21 in most cancers was significantly correlated with TMB, MSI, neoantigens and chromosomal ploidy, and in epigenetics, elevated NUDT21 expression was strongly associated with genomic stability, mismatch repair genes, tumor stemness, and RNA methylation. Based on immunosuppressive score, we found that NUDT21 plays an essential role in the immunosuppressive environment by suppressing immune checkpointing effect in most cancers. In addition, using HHNSCC as a study target, PCR and pathological detection of NUDT21 in tumor tissues was significantly increased than that in paracancerous normal tissues. In vitro cellular assays, silencing NUDT21 inhibited proliferation and promoted apoptosis in FaDu and CNE-2Z cells, and blocked the cell cycle in the G2/M phase. Therefore, the experiments confirmed that NUDT21 promotes the proliferation of FaDu by suppressing the expression of apoptotic.

The microRNA target site profile is a novel biomarker in the immunotherapy response

MicroRNAs (miRNAs) bind on the 3' untranslated region (3'UTR) of messenger RNAs (mRNAs) and regulate mRNA expression in physiological and pathological conditions, including cancer. Thus, studies have identified miRNAs as potential biomarkers by correlating the miRNA expression with the expression of important mRNAs and/or clinical outcomes in cancers. However, tumors undergo pervasive 3'UTR shortening/lengthening events through alternative polyadenylation (APA), which varies the number of miRNA target sites in mRNA, raising the number of miRNA target sites (numTS) as another important regulatory axis of the miRNA binding effects. In this study, we developed the first statistical method, BIOMATA-APA, to identify predictive miRNAs based on numTS features. Running BIOMATA-APA on The Cancer Genome Atlas (TCGA) and independent cohort data both with immunotherapy and no immunotherapy, we demonstrated for the first time that the numTS feature 1) distinguishes different cancer types, 2) predicts tumor proliferation and immune infiltration status, 3) explains more variation in the proportion of tumor-infiltrating immune cells, 4) predicts response to immune checkpoint blockade (ICB) therapy, and 5) adds prognostic power beyond clinical and miRNA expression. To the best of our knowledge, this is the first pan-cancer study to systematically demonstrate numTS as a novel type of biomarker representing the miRNA binding effects underlying tumorigenesis and pave the way to incorporate miRNA target sites for miRNA biomarker identification. Another advantage of examining the miRNA binding effect using numTS is that it requires only RNA-Seq data, not miRNAs, thus resulting in high power in the miRNA biomarker identification.

Relationship between NUDT21 mediated alternative polyadenylation process and tumor

Alternative polyadenylation (APA) is a molecular process that generates diversity at the 3' end of RNA polymerase II transcripts from over 60% of human genes. APA and microRNA regulation are both mechanisms of post-transcriptional regulation of gene expression. As a key molecular mechanism, Alternative polyadenylation often results in mRNA isoforms with the same coding sequence but different lengths of 3' UTRs, while microRNAs regulate gene expression by binding to specific mRNA 3' UTRs. Nudix Hydrolase 21 (NUDT21) is a crucial mediator involved in alternative polyadenylation (APA). Different studies have reported a dual role of NUDT21 in cancer (both oncogenic and tumor suppressor). The present review focuses on the functions of APA, miRNA and their interaction and roles in development of different types of tumors.NUDT21 mediated 3' UTR-APA changes can be used to generate specific signatures that can be used as potential biomarkers in development and disease. Due to the emerging role of NUDT21 as a regulator of the aforementioned RNA processing events, modulation of NUDT21 levels may be a novel viable therapeutic approach.

Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems

BACKGROUND

Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformatic methods of causal inference cannot identify the nonlinear relationships and estimate their effect size.

RESULTS

To overcome these limitations, we developed the first computational method that explicitly learns nonlinear causal relations and estimates the effect size using a deep neural network approach coupled with the knockoff framework, named causal directed acyclic graphs using deep learning variable selection (DAG-deepVASE). Using simulation data of diverse scenarios and identifying known and novel causal relations in molecular and clinical data of various diseases, we demonstrated that DAG-deepVASE consistently outperforms existing methods in identifying true and known causal relations. In the analyses, we also illustrate how identifying nonlinear causal relations and estimating their effect size help understand the complex disease pathobiology, which is not possible using other methods.

CONCLUSIONS

With these advantages, the application of DAG-deepVASE can help identify driver genes and therapeutic agents in biomedical studies and clinical trials.

Alternative splicing and alternative polyadenylation define tumor immune microenvironment and pharmacogenomic landscape in clear cell renal carcinoma

Two major posttranscriptional mechanisms—alternative splicing (AS) and alternative polyadenylation (APA)—have attracted much attention in cancer research. Nevertheless, their roles in clear cell renal carcinoma (ccRCC) are still ill defined. Herein, this study was conducted to uncover the implications of AS and APA events in ccRCC progression. Through consensus molecular clustering analysis, two AS or APA RNA processing phenotypes were separately constructed with distinct prognosis, tumor-infiltrating immune cells, responses to immunotherapy, and chemotherapy. The AS or APA score was constructed to quantify AS or APA RNA processing patterns of individual ccRCCs with principal-component analysis. Both high AS and APA scores were characterized by undesirable survival outcomes, relatively high response to immunotherapy, and low sensitivity to targeted drugs, such as sorafenib and pazopanib. Moreover, several small molecular compounds were predicted for patients with a high AS or APA score. There was a positive correlation between AS and APA scores. Their interplay contributed to poor prognosis and reshaped the tumor immune microenvironment. Collectively, this study is the first to comprehensively analyze two major posttranscriptional events in ccRCC. Our findings uncovered the potential functions of AS and APA events and identified their therapeutic potential in immunotherapy and targeted therapy.

Cleavage factor Im 25 as a potential biomarker for prognosis of colorectal cancer

Background

Cleavage factor Im 25 (CFIm25) affects the prognosis and progression of cancer by regulating alternative polyadenylation; however, its role in colorectal cancer remains unclear.

Methods

A standard EnVision tissue microarray was used to evaluate the expression of CFIm25 by immunohistochemistry in 363 patients with colorectal cancer. The correlation between CFIm25 expression and clinicopathological characteristics was analyzed using the χ² test. Univariate analysis was used to study the relationship between clinicopathological characteristics and patient prognosis. Multivariate analysis was performed using the Cox regression model to identify independent prognostic factors for patients with colorectal cancer.

Results

Statistical analysis revealed that CFIm25 expression was significantly associated with vascular invasion (P=0.000), serous invasion (P=0.007), pT stage (P=0.016), and clinical stage (P=0.007). Age, vascular invasion, nerve invasion, serosal invasion, differentiation, clinical stage, recurrence, and CFIm25 expression were significantly correlated with the survival time of colorectal cancer patients (P<0.05). The mean overall survival rate in colorectal cancer patients with decreased CFIm25 expression was only 88.53 months, compared with 110.69 months in the high expression group (P=0.000). Decreased CFIm25 expression indicated a worse prognosis in patients with colorectal cancer. Further analysis by the Cox multivariate model showed that CFIm25 (HR, 0.543; 95% CI: 0.372–0.792; P=0.002) and serosa invasion (HR, 1.470; 95% CI: 1.032–2.094; P=0.033) are independent prognostic factors for colorectal cancer.

Conclusions

Decreased CFIm25 expression indicates a worse prognosis of colorectal cancer patients and could be a novel target for the treatment of colorectal cancer in the future.

Keywords

Polyadenylation; survival analysis; colorectal cancer (CRC); CFIm25

NUDT21 Promotes Tumor Growth and Metastasis Through Modulating SGPP2 in Human Gastric Cancer

Gastric cancer is one of the major malignancies with poor survival outcome. In this study, we reported that NUDT21 promoted cell proliferation, colony formation, cell migration and invasion in gastric cancer cells. The expression levels of NUDT21 were found to be much higher in human gastric cancer tissues compared with normal gastric tissues. NUDT21 expression was positively correlated with tumor size, lymph node metastasis and clinical stage in gastric cancer patients. High level of NUDT21 was associated with poor overall survival (OS) rates in gastric cancer patients. The expression levels of NUDT21 were also much higher in gastric cancer tissues from patients with tumor metastasis compared with those of patients without tumor metastasis. Moreover, forced expression of NUDT21 in gastric cancer cells promoted tumor growth and cell proliferation in xenograft nude mice, and depletion of NUDT21 in gastric cancer cells restrained lung metastasis in vivo. Through high throughput RNA-sequencing, SGPP2 was identified to be positively regulated by NUDT21 and mediated the tumor promoting role of NUDT21 in gastric cancer cells. Therefore, NUDT21 played an oncogenic role in human gastric cancer cells. NUDT21 could be considered as a novel potential target for gastric cancer therapy.

Interplay within tumor microenvironment orchestrates neoplastic RNA metabolism and transcriptome diversity

The heterogeneous population of cancer cells within a tumor mass interacts intricately with the multifaceted aspects of the surrounding microenvironment. The reciprocal crosstalk between cancer cells and the tumor microenvironment (TME) shapes the cancer pathophysiome in a way that renders it uniquely suited for immune tolerance, angiogenesis, metastasis, and therapy resistance. This dynamic interaction involves a dramatic reconstruction of the transcriptomic landscape of tumors by altering the synthesis, modifications, stability, and processing of gene readouts. In this review, we categorically evaluate the influence of TME components, encompassing a myriad of resident and infiltrating cells, signaling molecules, extracellular vesicles, extracellular matrix, and blood vessels, in orchestrating the cancer-specific metabolism and diversity of both mRNA and noncoding RNA, including micro RNA, long noncoding RNA, circular RNA among others. We also highlight the transcriptomic adaptations in response to the physicochemical idiosyncrasies of TME, which include tumor hypoxia, extracellular acidosis, and osmotic stress. Finally, we provide a nuanced analysis of existing and prospective therapeutics targeting TME to ameliorate cancer-associated RNA metabolism, consequently thwarting the cancer progression. This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA Turnover and Surveillance > Regulation of RNA Stability RNA in Disease and Development > RNA in Disease.