RBFOX3 Promotes Gastric Cancer Growth and Progression by Activating HTERT Signaling

The Relevance of Telomerase and Telomere-Associated Proteins in B-Acute Lymphoblastic Leukemia

Telomeres and telomerase are closely linked to uncontrolled cellular proliferation, immortalization and carcinogenesis. Telomerase has been largely studied in the context of cancer, including leukemias. Deregulation of human telomerase gene hTERT is a well-established step in leukemia development. B-acute lymphoblastic leukemia (B-ALL) recovery rates exceed 90% in children; however, the relapse rate is around 20% among treated patients, and 10% of these are still incurable. This review highlights the biological and clinical relevance of telomerase for B-ALL and the implications of its canonical and non-canonical action on signaling pathways in the context of disease and treatment. The physiological role of telomerase in lymphocytes makes the study of its biomarker potential a great challenge. Nevertheless, many works have demonstrated that high telomerase activity or hTERT expression, as well as short telomeres, correlate with poor prognosis in B-ALL. Telomerase and related proteins have been proven to be promising pharmacological targets. Likewise, combined therapy with telomerase inhibitors may turn out to be an alternative strategy for B-ALL.

An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma

METHODS

Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the k-means clustering algorithm to obtain different subgroups for evaluating the prognosis-related risk of stomach adenocarcinoma. The model's robustness was verified using a 10-fold cross-validation (CV). Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate Cox regression was used to estimate hazard risk. The model was validated in three independent cohorts with different endpoints.

RESULTS

The patients were divided into low-risk and high-risk groups according to the k-means clustering algorithm. The high-risk group had a significantly higher risk of poor survival (log-rank P value = 2.80e - 06; adjusted hazard ratio = 2.386, 95% confidence interval: 1.607~3.543), a concordance index (C-index) of 0.714, and a Brier score of 0.184. The model performed well both in the 10-fold CV procedure and three independent cohorts from the Gene Expression Omnibus (GEO) repository.

CONCLUSIONS

A robust and generalizable model based on the autoencoder was proposed to integrate multiomics data and predict the prognosis of patients with stomach adenocarcinoma. The model demonstrates better performance than two alternative approaches on prognosis prediction. The results might provide the grounds for further exploring the potential biomarkers to predict the prognosis of patients with stomach adenocarcinoma.

Integrating 13 Microarrays to Construct a 6 RNA-binding proteins Prognostic Signature for Gastric Cancer patients

Background: It has been confirmed in many tumors that RNA-binding proteins (RBPs) will affect the progress of cancer, but there is still a lack of large-scale research in gastric cancer (GC). Methods: We obtained 13 microarray mRNA expression profiles of the GPL570 platform, and extracted expression from them after integration to analyze the expression differences of RBPs. Enrichment analysis studies the role of these RBPs in GC. Univariate, Lasso and multivariate Cox regression analysis are used to identify independent prognostic hub RBPs, thereby constructing and verifying a prognostic signature. External data and rt-PCR verified the expression of hub RBPs. Results: We have identified 51 dysregulated RBPs in GC. Enrichment analysis shows that it can mainly participate in RNA decomposition, modification, processing, etc. and affect the progress of GC. After multiple statistical analysis, six independent prognostic RBPs of GC were determined and a prognostic signature was developed. According to the median risk value, the training cohort was divided into high-risk and low-risk groups. Considering the clinical characteristics, in training, testing, and complete cohorts, the overall survival rate of the high-risk group was significantly lower than that of the low-risk group, which was confirmed by the time-dependent receiver operating characteristic curve. Univariate and multivariate Cox regression analysis of independent prognostic ability of risk score. In addition, we constructed and verified a nomogram based on the prognostic signature, showing accurate prediction performance. rt-PCR and external data verification are consistent with our conclusions. Conclusion: This study analyzed the overall expression of RPBs in GC and explored its mechanism. A new prognostic signature was developed and verified. A nomogram has also been established and verified, which helps to improve the treatment strategy for GC.

Malignant Evaluation and Clinical Prognostic Values of M6A RNA Methylation Regulators in Prostate Cancer

Objective: M6A RNA modification is closely associated with tumor genesis and progression of several malignancies; however, its role in prostate cancer (PCa) remains poorly understood.

Materials and methods: Expression data and corresponding clinicopathologic information were available freely from the Cancer Genome Atlas (TCGA) dataset. We compared the expression level of m6A RNA methylation regulators in PCa with different clinicopathologic characteristics and identified subgroups based on their expressions with consensus clustering. To build the signature and assess its prognostic value, several methods were used for the analysis, including univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, time-dependent receiver operating curve (ROC), and Kaplan-Meier (KM) survival analysis.

Results: Most of the m6A RNA methylation regulators were differentially expressed not only between normal and tumor tissue but also among PCa stratified by different clinicopathologic characteristics. There were obvious differences between two clusters, cluster 1 and 2, regarding clinicopathologic features, and the recurrence-free survival (RFS) in cluster 2 was significantly worse than cluster 1. We developed an eleven-gene signature which exhibited a high prognostic value and was able to independently predict RFS. Moreover, a nomogram which integrated clinical information and the gene signature was capable of distinguishing high-risk recurrent patients.

Conclusion: These methylation regulators are correlated to clinicopathologic characteristics in PCa and a prognostic model using m6A methylation-related genes is constructed and of high predictive value for recurrence after RP.